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总结Prompt&LLM论文，开源数据&模型，AIGC应用

demonstration in-context-learning prompt few-shot-learning zero-shot-learning aigc papers prompt-tuning instruction-tuning chain-of-thought

decryptprompt's Introduction

DecryptPrompt

如果LLM的突然到来让你感到沮丧，不妨读下主目录的Choose Your Weapon Survival Strategies for Depressed AI Academics 持续更新以下内容，Star to keep updated~

目录顺序如下

  1. 国内外，垂直领域大模型
  2. Agent和指令微调等训练框架
  3. 开源指令，预训练，rlhf，对话，agent训练数据梳理
  4. AIGC相关应用
  5. prompt写作指南和5星博客等资源梳理
  6. Prompt和LLM论文细分方向梳理

My blogs

LLMS

模型评测

榜单 结果
AlpacaEval：LLM-based automatic evaluation 开源模型王者vicuna,openchat, wizardlm
Huggingface Open LLM Leaderboard MMLU只评估开源模型，Falcon夺冠，在Eleuther AI4个评估集上评估的LLM模型榜单,vicuna夺冠
https://opencompass.org.cn/ 上海人工智能实验室推出的开源榜单
Berkley出品大模型排位赛榜有准中文榜单 Elo评分机制，GPT4自然是稳居第一，GPT4>Claude>GPT3.5>Vicuna>others
CMU开源聊天机器人评测应用 ChatGPT>Vicuna>others；在对话场景中训练可能很重要
Z-Bench中文真格基金评测 国产中文模型的编程可用性还相对较低，大家水平差不太多，两版ChatGLM提升明显
Chain-of-thought评估 GSM8k, MATH等复杂问题排行榜
InfoQ 大模型综合能力评估 面向中文，ChatGPT>文心一言> Claude>星火
ToolBench: 工具调用评估榜单 工具微调模型和ChatGPT进行对比，提供评测脚本
AgentBench: 推理决策评估榜单 清华联合多高校推出不同任务环境，例如购物，家居，操作系统等场景下模型推理决策能力
FlagEval 智源出品主观+客观LLM评分榜单
Bird-Bench 更贴合真实世界应用的超大数据库，需要领域知识的NL2SQL榜单，模型追赶人类尚有时日
kola 以世界知识为核心的评价基准，包括已知的百科知识和未知的近90天网络发布内容，评价知识记忆，理解，应用和创造能力
CEVAL 中文知识评估，覆盖52个学科，机器评价主要为多项选择
CMMLU 67个主题中文知识和推理能力评估，多项选择机器评估
LLMEval3 复旦推出的知识问答榜单，涵盖大学作业和考题，题库尽可能来自非互联网避免模型作弊
FinancelQ 度小满开源的金融多项选择评估数据集
SWE-bench 基于真实github问题和PR的模型编程能力评估
Awesome-MLLM 多模态大模型榜单

国外开源模型

模型链接 模型描述
Phi-3-MINI-128K 还是质量>数量的训练逻辑，微软的3B小模型
LLama3 Open Meta带着可商用开源的羊驼3模型来了，重回王座~
WizardLM-2-8x22B 微软带着WizardLM-2也来了包括70B,7B 和8*22B
OpenSora 没等来OpenAI却等来了OpenSora这个梗不错哦
GROK 马斯克开源Grok-1：3140亿参数迄今最大，权重架构全开放
Gemma 谷歌商场开源模型2B，7B免费商用
Mixtral8*7B 法国“openai”开源基于MegaBlocks训练的MOE模型8*7B 32K
Mistral7B 法国“openai”开源Mistral，超过llama2当前最好7B模型
Idefics2 Hugging Face 推出 Idefics2 8B 多模态模型
Dolphin-2.2.1-Mistral-7B 基于Mistral7B使用dolphin数据集微调
Falcon Falcon由阿联酋技术研究所在超高质量1万亿Token上训练得到1B，7B，40B开源，免费商用！土豪们表示钱什么的格局小了
Vicuna Alpaca前成员等开源以LLama13B为基础使用ShareGPT指令微调的模型，提出了用GPT4来评测模型效果
OpenChat 80k ShareGPT对话微调LLama-2 13B开源模型中的战斗机
Guanaco LLama 7B基座，在alpaca52K数据上加入534K多语言指令数据微调
MPT MosaicML开源的预训练+指令微调的新模型，可商用，支持84k tokens超长输入
RedPajama RedPajama项目既开源预训练数据后开源3B，7B的预训练+指令微调模型
koala 使用alpaca，HC3等开源指令集+ ShareGPT等ChatGPT数据微调llama，在榜单上排名较高
ChatLLaMA 基于RLHF微调了LLaMA
Alpaca 斯坦福开源的使用52k数据在7B的LLaMA上微调得到，
Alpaca-lora LORA微调的LLaMA
Dromedary IBM self-aligned model with the LLaMA base
ColossalChat HPC-AI Tech开源的Llama+RLHF微调
MiniGPT4 Vicuna+BLIP2 文本视觉融合
StackLLama LLama使用Stackexchange数据+SFT+RL
Cerebras Cerebras开源了1亿到130亿的7个模型，从预训练数据到参数全开源
Dolly-v2 可商用 7b指令微调开源模型在GPT-J-6B上微调
OpenChatKit openai研究员打造GPT-NoX-20B微调+6B审核模型过滤
MetaLM 微软开源的大规模自监督预训练模型
Amazon Titan 亚马逊在aws上增加自家大模型
OPT-IML Meta复刻GPT3，up to 175B, 不过效果并不及GPT3
Bloom BigScience出品，规模最大176B
BloomZ BigScience出品, 基于Bloom微调
Galacia 和Bloom相似，更针对科研领域训练的模型
T0 BigScience出品，3B~11B的在T5进行指令微调的模型
EXLLama Python/C++/CUDA implementation of Llama for use with 4-bit GPTQ weight
LongChat llama-13b使用condensing rotary embedding technique微调的长文本模型
MPT-30B MosaicML开源的在8Ktoken上训练的大模型

国内开源模型

模型链接 模型描述
Yuan2.0-M32 原2.0 M32MOE 大模型
DeepSeek-v2 深度求索最新发布的21B MOE超强大模型降低KV-cache推理更高效
Qwen1.5-MoE-A2.7B Qwen推出MOE版本，推理更快
Qwen1.5 通义千问升级1.5，支持32K上文
Baichuan2 百川第二代也出第二个版本了，提供了7B/13B Base和chat的版本
ziya2 基于Llama2训练的ziya2它终于训练完了
InternLM2 7B+20B 商汤的书生模型2支持200K
InternLM-XComposer 最新多模态视觉大模型
Orion-14B-LongChat 猎户星空多语言模型支持320K
ChatGLM3 ChatGLM3发布，支持工具调用等更多功能，不过泛化性有待评估
Yuan-2.0 浪潮发布Yuan2.0 2B，51B，102B
YI-200K 元一智能开源超长200K的6B，34B模型
XVERSE-256K 元象发布13B免费商用大模型，虽然很长但是
LLama2-chinese 没等太久中文预训练微调后的llama2它来了~
YuLan-chat2 高瓴人工智能基于Llama-2中英双语继续预训练+指令微调/对话微调
BlueLM Vivo人工智能实验室开源大模型
zephyr-7B HuggingFace 团队基于 UltraChat 和 UltraFeedback 训练了 Zephyr-7B 模型
XWin-LM llama2 + SFT + RLHF
Skywork 昆仑万维集团·天工团队开源13B大模型可商用
Chinese-LLaMA-Alpaca 哈工大中文指令微调的LLaMA
Moss 为复旦正名！开源了预训练，指令微调的全部数据和模型。可商用
InternLM 书生浦语在过万亿 token 数据上训练的多语千亿参数基座模型
Aquila2 智源更新Aquila2模型系列包括全新34B
Aquila 智源开源7B大模型可商用免费
UltraLM系列 面壁智能开源UltraLM13B，奖励模型UltraRM，和批评模型UltraCM
PandaLLM LLAMA2上中文wiki继续预训练+COIG指令微调
XVERSE 据说中文超越llama2的元象开源模型13B模型
BiLLa LLama词表·扩充预训练+预训练和任务1比1混合SFT+指令样本SFT三阶段训练
Phoenix 港中文开源凤凰和奇美拉LLM，Bloom基座，40+语言支持
Wombat-7B 达摩院开源无需强化学习使用RRHF对齐的语言模型, alpaca基座
TigerBot 虎博开源了7B 180B的模型以及预训练和微调语料
Luotuo 中文指令微调的LLaMA，和ChatGLM
OpenBuddy Llama 多语言对话微调模型
Chinese Vincuna LLama 7B基座，使用Belle+Guanaco数据训练
Linly Llama 7B基座，使用belle+guanaco+pclue+firefly+CSL+newscommentary等7个指令微调数据集训练
Firefly 中文2.6B模型，提升模型中文写作，古文能力，待开源全部训练代码，当前只有模型
Baize 使用100k self-chat对话数据微调的LLama
BELLE 使用ChatGPT生成数据对开源模型进行中文优化
Chatyuan chatgpt出来后最早的国内开源对话模型，T5架构是下面PromptCLUE的衍生模型
PromptCLUE 多任务Prompt语言模型
PLUG 阿里达摩院发布的大模型，提交申请会给下载链接
CPM2.0 智源发布CPM2.0
GLM 清华发布的中英双语130B预训练模型
BayLing 基于LLama7B/13B，增强的语言对齐的英语/中文大语言模型

开源多模态模型

模型 描述
Kosmos-2.5 微软推出的多模态擅长识别多文字、表格图片
LLAVA-1.5 升级后的LLAVA 13B模型浙大出品
MiniGPT-4 认知类任务评分最高
InternLM-XComposer 书生浦语·灵笔2，擅长自由图文理解
mPLUG-DocOwl 阿里出品面向文档理解的多模态模型

LLM免费应用

模型链接 模型描述
PPLX-7B/70B Perplexity.ai的Playground支持他们自家的PPLX模型和众多SOTA大模型，Gemma也支持了
kimi Chat Moonshot超长文本LLM 可输入20W上文, 文档总结无敌
万知 YI模型基座的应用，支持OCR文档识别
跃问 阶跃星辰推出的同样擅长长文本的大模型
讯飞星火 科大讯飞
文心一言 百度
通义千问 阿里
百川 百川
ChatGLM 智谱轻言
DeepSeek 深度求索
360智脑 360
悟空 字节跳动

垂直领域模型&进展

领域 模型链接 模型描述
医疗 MedGPT 医联发布的
医疗 MedPalm Google在Faln-PaLM的基础上通过多种类型的医疗QA数据进行prompt-tuning指令微调得到，同时构建了MultiMedQA
医疗 ChatDoctor 110K真实医患对话样本+5KChatGPT生成数据进行指令微调
医疗 Huatuo Med-ChatGLM 医学知识图谱和chatgpt构建中文医学指令数据集+医学文献和chatgpt构建多轮问答数据
医疗 Chinese-vicuna-med Chinese-vicuna在cMedQA2数据上微调
医疗 OpenBioMed 清华AIR开源轻量版BioMedGPT, 知识图谱&20+生物研究领域多模态预训练模型
医疗 DoctorGLM ChatDoctor+MedDialog+CMD 多轮对话+单轮指令样本微调GLM
医疗 MedicalGPT-zh 自建的医学数据库ChatGPT生成QA+16个情境下SELF构建情景对话
医疗 PMC-LLaMA 医疗论文微调Llama
医疗 PULSE Bloom微调+继续预训练
医疗 NHS-LLM Chatgpt生成的医疗问答，对话，微调模型
医疗 神农医疗大模型 以中医知识图谱的实体为中心生成的中医知识指令数据集11w+，微调LLama-7B
医疗 岐黄问道大模型 3个子模型构成，已确诊疾病的临床治疗模型+基于症状的临床诊疗模型+中医养生条理模型，看起来是要ToB落地
医疗 Zhongjing 基于Ziya-LLama+医疗预训练+SFT+RLHF的中文医学大模型
医疗 MeChat 心理咨询领域，通过chatgpt改写多轮对话56k
医疗 SoulChat 心理咨询领域中文长文本指令与多轮共情对话数据联合指令微调 ChatGLM-6B
医疗 MindChat MindChat-Baichuan-13B,Qwen-7B,MindChat-InternLM-7B使用不同基座在模型安全，共情，人类价值观对其上进行了强化
医疗 DISC-MedLLM 疾病知识图谱构建QA对+QA对转化成单论对话+真实世界数据重构+人类偏好数据筛选，SFT微调baichuan
法律 LawGPT-zh 利用ChatGPT清洗CrimeKgAssitant数据集得到52k单轮问答+我们根据中华人民共和国法律手册上最核心的9k法律条文，利用ChatGPT联想生成具体的情景问答+知识问答使用ChatGPT基于文本构建QA对
法律 LawGPT 基于llama+扩充词表二次预训练+基于法律条款构建QA指令微调
法律 Lawyer Llama 法律指令微调数据集：咨询+法律考试+对话进行指令微调
法律 LexiLaw 法律指令微调数据集：问答+书籍概念解释，法条内容进行指令微调
法律 ChatLaw 北大推出的法律大模型，应用形式很新颖类似频道内流一切功能皆融合在对话形式内
法律 录问模型 在baichuan基础上40G二次预训练+100K指令微调，在知识库构建上采用了Emb+意图+关键词联想结合的方案
金融 OpenGPT 领域LLM指令样本生成+微调框架
金融 乾元BigBang金融2亿模型 金融领域预训练+任务微调
金融 度小满千亿金融大模型 在Bloom-176B的基础上进行金融+中文预训练和微调
金融 聚宝盆 基于 LLaMA 系基模型经过中文金融知识指令精调/指令微调(Instruct-tuning) 的微调模型
金融 PIXIU 整理了多个金融任务数据集加入了时间序列数据进行指令微调
金融 FinGPT 金融传统任务微调 or chatgpt生成金融工具调用
金融 CFGPT 金融预训练+指令微调+RAG等检索任务增强
金融 DISC-FinLLM 复旦发布多微调模型组合金融系统，包括金融知识问答,金融NLP任务，金融计算，金融检索问答
金融 InvestLM CFA考试，SEC， StackExchange投资问题等构建的金融指令微调LLaMA-65+
金融 DeepMoney 基于yi-34b-200k使用金融研报进行微调
编程 Starcoder 80种编程语言+Issue+Commit训练得到的编程大模型
编程 ChatSQL 基于ChatGLM实现NL2sql
编程 codegeex 13B预训练+微调多语言变成大模型
编程 codegeex2 Chatglm2的基础上CodeGeeX2-6B 进一步经过了 600B 代码数据预训练
编程 stabelcode 560B token多语言预训练+ 120,000 个 Alpaca指令对齐
编程 SQLCoder 在StarCoder的基础上微调15B超越gpt3.5
数学 MathGPT 是好未来自主研发的，面向全球数学爱好者和科研机构，以解题和讲题算法为核心的大模型。
数学 MammoTH 通过COT+POT构建了MathInstruct数据集微调llama在OOD数据集上超越了WizardLM
数学 MetaMath 模型逆向思维解决数学问题，构建了新的MetaMathQA微调llama2
交通 TransGPT LLama-7B+34.6万领域预训练+5.8万条领域指令对话微调（来自文档问答）
交通 TrafficGPT ChatGPT+Prompt实现规划，调用交通流量领域专业TFM模型，TFM负责数据分析，任务执行，可视化等操
科技 Mozi 红睡衣预训练+论文QA数据集 + ChatGPT扩充科研对话数据
天文 StarGLM 天文知识指令微调，项目进行中后期考虑天文二次预训练+KG
写作 阅文-网文大模型介绍 签约作者内测中，主打的内容为打斗场景，剧情切换，环境描写，人设，世界观等辅助片段的生成
写作 MediaGPT LLama-7B扩充词表+指令微调，指令来自国内媒体专家给出的在新闻创作上的80个子任务
电商 EcomGPT 电商领域任务指令微调大模型，指令样本250万，基座模型是Bloomz
植物科学 PLLaMa 基于Llama使用植物科学领域学术论文继续预训练+sft扩展的领域模型
评估 Auto-J 上交开源了价值评估对齐13B模型
评估 JudgeLM 智源开源了 JudgeLM 的裁判模型，可以高效准确地评判各类大模型
评估 CritiqueLLM 智谱AI发布评分模型CritiqueLLM,支持含参考文本/无参考文本的评估打分

Tool and Library

推理框架

工具描述 链接
FlexFlow：模型部署推理框架 https://github.com/flexflow/FlexFlow
Medusa：针对采样解码的推理加速框架，可以和其他策略结合 https://github.com/FasterDecoding/Medusa
FlexGen: LLM推理 CPU Offload计算架构 https://github.com/FMInference/FlexGen
VLLM：超高速推理框架Vicuna，Arena背后的无名英雄，比HF快24倍，支持很多基座模型 https://github.com/vllm-project/vllm
Streamingllm: 新注意力池Attention方案，无需微调拓展模型推理长度，同时为推理提速 https://github.com/mit-han-lab/streaming-llm
llama2.c: llama2 纯C语言的推理框架 https://github.com/karpathy/llama2.c
Guidance: 大模型推理控制框架，适配各类interleave生成 https://github.com/guidance-ai/guidance

指令微调，预训练，rlhf框架

工具描述 链接
LoRA：Low-Rank指令微调方案 https://github.com/tloen/alpaca-lora
peft：parameter-efficient prompt tunnging工具集 https://github.com/huggingface/peft
RL4LMs：AllenAI的RL工具 https://github.com/allenai/RL4LMs
RLLTE：港大，大疆等联合开源RLLTE开源学习框架 https://github.com/RLE-Foundation/rllte
trl：基于Transformer的强化训练框架 https://github.com/lvwerra/trl
trlx：分布式训练trl https://github.com/CarperAI/trlx
北大开源河狸项目可复现RLHF，支持多数LLM，提供RLHF数据 https://github.com/PKU-Alignment/safe-rlhf
RL4LMs：AllenAI的RL工具 https://github.com/allenai/RL4LMs
LMFlow：港科大实验室开源的大模型微调框架，支持以上多数开源模型的指令微调和RLHF https://github.com/OptimalScale/LMFlow
hugNLP:基于Huggingface开发继承Prompt技术，预训练和是指输入等多种方案 https://github.com/wjn1996/HugNLP
Deepspeed：针对RL训练和推理的整合优化 https://github.com/microsoft/DeepSpeed
Uerpy:预训练框架支持lm,mlm,unilm等 https://github.com/dbiir/UER-py
TecentPretrain: Uerpy的重构版本支持llama预训练 https://github.com/Tencent/TencentPretrain/tree/main
lamini: 整合指令数据生成，SFT，RLHF的工具库 https://github.com/lamini-ai/lamini/
Chain-of-thought-hub：模型推理能力评估平台 https://github.com/FranxYao/chain-of-thought-hub
EasyEdit：浙大开源支持多种模型，多种方案的模型知识精准编辑器 https://github.com/zjunlp/EasyEdit
OpenDelta：集成了各种增量微调方案的开源实现 https://github.com/thunlp/OpenDelta
Megablocks：MOE训练框架 https://github.com/stanford-futuredata/megablocks
Tutel：MOE训练框架 https://github.com/microsoft/tutel
LongLora: 长文本微调框架 https://github.com/dvlab-research/LongLoRA
LlamaGym：在线RL微调框架 https://github.com/KhoomeiK/LlamaGym
Megatron-LM：主流LLM预训练框架 https://github.com/NVIDIA/Megatron-LM
TradingGym：参考openai gym的股票交易强化学习模拟器 https://github.com/astrologos/tradinggym
TradeMaster: 量化交易RL训练框架 https://github.com/TradeMaster-NTU/TradeMaster
REFT：大模型表征微调框架 https://github.com/stanfordnlp/pyreft

Auto/Multi Agent

工具描述 链接
AutoGen：微软开源多Agent顶层框架 https://github.com/microsoft/autogen
CrewAI: 比chatDev流程定义更灵活的多智能体框架 https://github.com/joaomdmoura/CrewAI
ChatDev: 面壁智能开源多智能体协作的虚拟软件公司 https://github.com/OpenBMB/ChatDev
Generative Agents:斯坦福AI小镇的开源代码 https://github.com/joonspk-research/generative_agents
BabyAGI：自执行LLM Agent https://github.com/yoheinakajima/babyagi
AutoGPT：自执行LLM Agent https://github.com/Torantulino/Auto-GPT
AutoGPT-Plugins：提供众多Auo-GPT官方和第三方的插件 https://github.com/Significant-Gravitas/Auto-GPT-Plugins
XAgent: 面壁智能开源双循环AutoGPT https://github.com/OpenBMB/XAgent
MetaGPT: 覆盖软件公司全生命流程，例如产品经理等各个职业的AutoGPT https://github.com/geekan/MetaGPT
ResearchGPT: 论文写作领域的AutoGPT，融合论文拆解+网络爬虫 https://github.com/assafelovic/gpt-researcher
MiniAGI：自执行LLM Agent https://github.com/muellerberndt/mini-agi
AL Legion： 自执行LLM Agent https://github.com/eumemic/ai-legion
AgentVerse：多模型交互环境 https://github.com/OpenBMB/AgentVerse
AgentSims: 给定一个社会环境，评估LLM作为智能体的预定任务目标完成能力的沙盒环境 https://github.com/py499372727/AgentSims/
GPTRPG：RPG环境 AI Agent游戏化 https://github.com/dzoba/gptrpg
GPTeam：多智能体交互 https://github.com/101dotxyz/GPTeam
GPTEngineer：自动工具构建和代码生成 https://github.com/AntonOsika/gpt-engineer
WorkGPT：类似AutoGPT https://github.com/team-openpm/workgpt
AI-Town: 虚拟世界模拟器 https://github.com/a16z-infra/ai-town
webarena:网络拟真环境，可用于自主智能体的测试，支持在线购物，论坛，代码仓库etc https://github.com/web-arena-x/webarena
MiniWoB++：100+web交互操作的拟真环境 https://github.com/Farama-Foundation/miniwob-plusplus
VIRL:虚拟世界模拟器 https://github.com/VIRL-Platform/VIRL

Agent工具框架类

工具描述 链接
OpenAgents: 开源版ChatGPT-Plus搭建框架 https://github.com/xlang-ai/OpenAgents
LangGraph：白盒话，可循环基于有向无环图的Agent工作流构建框架 https://langchain-ai.github.io/langgraph/
langchain：LLM Agent框架 https://github.com/hwchase17/langchain
llama index：LLM Agent框架 https://github.com/jerryjliu/llama_index
Langroid: LLM Agent框架 https://github.com/langroid/langroid
Ragas: 评估检索增强LLM效果的框架，基于大模型prompt评估事实性，召回相关性，召回内容质量，回答相关性等 https://github.com/explodinggradients/ragas#fire-quickstart
fastRAG：检索框架，包括多索引检索，KG构建等基础功能 https://github.com/IntelLabs/fastRAG/tree/main
langflow：把langchain等agent组件做成了可拖拽式的UI https://github.com/logspace-ai/langflow
PhiData：把工具调用抽象成function call的Agent框架 https://github.com/phidatahq/phidata
Haystack: LLM Agent 框架，pipeline的设计模式个人感觉比langchain更灵活更简洁 https://github.com/deepset-ai/haystack
EdgeChain: 通过Jsonnet配置文件实现LLM Agent https://github.com/arakoodev/EdgeChains/tree/main
semantic-kernel：整合大模型和编程语言的SDK https://github.com/microsoft/semantic-kernel
BMTTools: 清华出品多工具调用开源库，提供微调数据和评估ToolBench https://github.com/OpenBMB/BMTools
Jarvis: 大模型调用小模型框架，给小模型一个未来！ https://github.com/search?q=jarvis
LLM-ToolMaker:让LLM自己制造Agent https://github.com/ctlllll/LLM-ToolMaker
Gorilla: LLM调用大量API https://github.com/ShishirPatil/gorilla
Open-Interpreter：命令行聊天框架 https://github.com/KillianLucas/open-interpreter
AnythingLLM: langchain推出的支持本地部署开源模型的框架 https://github.com/Mintplex-Labs/anything-llm
PromptFlow：微软推出的大模型应用框架 https://github.com/microsoft/promptflow
Anakin：和Coze类似的Agent定制应用，插件支持较少但workflow使用起来更简洁 r
TaskingAI：API-Oriented的类似langchain的大模型应用框架 https://www.tasking.ai/
TypeChat：微软推出的Schema Engineering风格的应用框架 https://github.com/microsoft/TypeChat
DSPy：把稳定性低的prompt优化为参数化和模板化的提示技术 https://github.com/stanfordnlp/dspy
PipeCAT：加入语音的Agent框架 https://github.com/pipecat-ai/pipecat/tree/main
Khoj: 桌面Agent的个人助手可本地部署 https://docs.khoj.dev/
farfalle：本地搭载的RAG引擎 https://github.com/rashadphz/farfalle/tree/main
Verba：本地搭载的RAG引擎 https://github.com/weaviate/Verba
Vanna：本地搭载提供了从已有数据库构建NL2SQL所需RAG数据库的方案 https://github.com/vanna-ai/vanna
TaskWeaver: code-first 的Agent https://github.com/microsoft/TaskWeaver
QMedia：多模态检索框架 https://github.com/QmiAI/Qmedia?tab=readme-ov-file

Agent Bot [托拉拽中间层]

应用 链接
Coze：免费 https://www.coze.com/
Dify https://dify.ai/zh
Anakin https://app.anakin.ai/discover
FLowise https://github.com/FlowiseAI/Flowise/blob/main/README-ZH.md
Microsoft Power Automate https://www.microsoft.com/zh-cn/power-platform/products/power-automate
Mind Studio：有限使用 https://youai.ai/
QuestFlow：付费 https://www.questflow.ai/

RAG，Agent配套工具

工具 描述
Alexandria 从Arix论文开始把整个互联网变成向量索引，可以免费下载
RapidAPI 统一这个世界的所有API，最大API Hub，有调用成功率，latency等，是真爱！
Composio 可以和langchain，crewAI等进行集成的工具API
PyTesseract OCR解析服务
EasyOCR 确实使用很友好的OCR服务
surya OCR服务
Vary 旷视多模态大模型pdf直接转Markdown
LLamaParse LLamaIndex提供的PDF解析服务，每天免费1000篇
Jina-Cobert Jian AI开源中英德，8192 Token长文本Embedding
BGE-M3 智源开源多语言，稀疏+稠密表征，8192 Token长文本Embedding
BCE 网易开源更适配RAG任务的Embedding模型
PreFLMR-VIT-G 剑桥开源多模态Retriever
openparse 文本解析分块开源服务，先分析文档的视觉布局再进行切分
layout-parser 准确度较高的开源OCR文档布局识别
AdvancedLiterateMachinery 阿里OCR团队的文档解析和图片理解
ragflow-deepdoc ragflow提供的文档识别和解析能力
FireCrawl 爬取url并生成markdown的神器
Jina-Reader 把网页转换成模型可读的格式
spRAG 注入上下文表征，和自动组合上下文提高完整性
knowledge-graph 自动知识图谱构建工具
Marker-API PDF转Markdwon服务

其他垂直领域Agent

工具描述 链接
GPT4v-ACT：基于JS DOM识别网页元素，服务于各类多模态webagent https://github.com/ddupont808/GPT-4V-Act?tab=readme-ov-file
Deep-KE：基于LLM对数据进行智能解析实现知识抽取 https://github.com/zjunlp/DeepKE
IncarnaMind：多文档RAG方案，动态chunking的方案可以借鉴 https://github.com/junruxiong/IncarnaMind
Vectra：平台化的LLM Agent搭建方案，从索引构建，内容召回排序，到事实检查的LLM生成 https://vectara.com/tour-vectara/
Data-Copilot：时间序列等结构化数据分析领域的Agent解决方案 https://github.com/zwq2018/Data-Copilot
DB-GPT: 以数据库为基础的GPT实验项目，使用本地化的GPT大模型与您的数据和环境进行交互 https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh_CN/latest/index.html
guardrails：降低模型幻觉的python框架，promp模板+validation+修正 https://github.com/shreyar/guardrails
guidance：微软新开源框架，同样是降低模型幻觉的框架，prompt+chain的升级版加入逐步生成和思维链路 https://github.com/guidance-ai/guidance
SolidGPT: 上传个人数据，通过命令交互创建项目PRD等 https://github.com/AI-Citizen/SolidGPT
HR-Agent: 类似HR和员工交互，支持多工具调用 https://github.com/stepanogil/autonomous-hr-chatbot
BambooAI：数据分析Agent https://github.com/pgalko/BambooAI
AlphaCodium：通过Flow Engineering完成代码任务 https://github.com/Codium-ai/AlphaCodium
REOR: AI驱动的笔记软件 https://github.com/reorproject/reor
Vanna.AI: chat with sql database https://vanna.ai/
ScrapeGraph：融合了图逻辑和LLM https://scrapegraph-doc.onrender.com/
OpenAct：Adapt-AI推出了的和桌面GUI交互的Agent框架 https://github.com/OpenAdaptAI/OpenAdapt
LaVague：WebAgent框架，偏低层指令交互性把指令转换成Selenium代码去和网页交互 https://github.com/lavague-ai/LaVague/tree/main
Tarsier: webagent的辅助工具把网站转换成可交互元素序号和描述 https://github.com/reworkd/tarsier?tab=readme-ov-file
RecAI：微软推出的推荐领域LLM Agent https://github.com/microsoft/RecAI
Skyvern: WebAgent框架 https://www.skyvern.com/
Translation Agent: 吴恩达开源的简单的翻译Agent，prompt也是用的XML格式 https://github.com/andrewyng/translation-agent/blob/main/src/translation_agent/utils.py
GPT-Computer-Assistant：和电脑直接进行交互的Agent基于Crewai https://github.com/onuratakan/gpt-computer-assistant

Training Data

数据类型 数据描述 数据链接
指令微调 self-instruct，GPT3自动生成&过滤得到指令集 https://github.com/yizhongw/self-instruct
指令微调 Standford Alpaca：52K text-davinci-003生成的self-instruct指令数据集 https://github.com/tatsu-lab/stanford_alpaca
指令微调 GPT4-for-LLM 中文+英文+对比指令 https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
指令微调 GPTTeacher更多样的通用指令，角色扮演和代码指令 https://github.com/teknium1/GPTeacher/tree/main
指令微调 中文翻译Alpaca还有一些其他指令数据集 https://github.com/hikariming/alpaca_chinese_dataset https://github.com/carbonz0/alpaca-chinese-dataset
指令微调 alpaca指令GPT4生成，和以上几版对比显著质量更高，回复更长 https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/tree/main
指令微调 Guanaco数据：对Alphca指令重写后以不同语言生成总共534K，有对话和非对话类型，还有补充的QA生成样本 https://huggingface.co/datasets/JosephusCheung/GuanacoDataset
指令微调 OIG中文指令包括翻译alpaca+natural+unnatural，多轮对话，考试，leetcode指令 https://github.com/BAAI-Zlab/COIG
指令微调 Vicuna训练使用的样本，用API获取了sharegpt上用户和chatgpt对话历史，部分网友整理到了HF https://github.com/domeccleston/sharegpt https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/tree/main
指令微调 HC3指令数据中英文，包括金融，开放QA，百科，DBQA，医学等包含人工回复 https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese/tree/main
指令微调 MOSS开源的SFT数据包含使用plugin的对话数据 https://huggingface.co/datasets/Hello-SimpleAI/HC3-Chinese/tree/main
指令微调 InstructWild数据：用四处爬取的chatgpt指令作为种子self-instruct扩充生成，中英双语 https://github.com/XueFuzhao/InstructionWild/tree/main/data
指令微调 BELLE100万指令数据，参考Alpaca用ChatGPT生成，有数学，多轮对话，校色对话等等 https://github.com/LianjiaTech/BELLE
指令微调 PromptCLUE多任务提示数据集：模板构建，只包含标准NLP任务 https://github.com/CLUEbenchmark/pCLUE
指令微调 TK-Instruct微调用的指令数据集, 全人工标注1600+NLP任务 https://instructions.apps.allenai.org/
指令微调 T0微调用的指令数据集（P3） https://huggingface.co/datasets/bigscience/P3
指令微调 p3衍生的46种多语言数据集（xmtf） https://github.com/bigscience-workshop/xmtf
指令微调 Unnatural Instruction使用GPT3生成后改写得到240k https://github.com/orhonovich/unnatural-instructions
指令微调 alpaca COT对多个数据源进行了清理并统一格式放到的了HF, 重点是人工整理的COT数据 https://github.com/PhoebusSi/Alpaca-CoT
指令微调 人工编写包含23种常见的中文NLP任务的指令数据，中文写作方向 https://github.com/yangjianxin1/Firefly
指令微调 Amazon COT指令样本包括各类QA，bigbench，math等 https://github.com/amazon-science/auto-cot
指令微调 CSL包含 396,209 篇中文核心期刊论文元信息 （标题、摘要、关键词、学科、门类）可做预训练可构建NLP指令任务 https://github.com/ydli-ai/CSL
指令微调 alpaca code 20K代码指令数据 https://github.com/sahil280114/codealpaca#data-release
指令微调 GPT4Tools 71K GPT4指令样本 https://github.com/StevenGrove/GPT4Tools
指令微调 GPT4指令+角色扮演+代码指令 https://github.com/teknium1/GPTeacher
指令微调 Mol-Instructions 2043K 分子+蛋白质+生物分子文本指令，覆盖分子设计、蛋白质功能预测、蛋白质设计等任务 https://github.com/zjunlp/Mol-Instructions
数学 腾讯人工智能实验室发布网上爬取的数学问题APE210k https://github.com/Chenny0808/ape210k
数学 猿辅导 AI Lab开源小学应用题Math23K https://github.com/SCNU203/Math23k/tree/main
数学 grade school math把OpenAI的高中数学题有改造成指令样本有2-8步推理过程 https://huggingface.co/datasets/qwedsacf/grade-school-math-instructions
数学 数学问答数据集有推理过程和多项选择 https://huggingface.co/datasets/math_qa/viewer/default/test?row=2
数学 AMC竞赛数学题 https://huggingface.co/datasets/competition_math
数学 线性代数等纯数学计算题 https://huggingface.co/datasets/math_dataset
代码 APPS从不同的开放访问编码网站Codeforces、Kattis 等收集的问题 https://opendatalab.org.cn/APPS
代码 Lyra代码由带有嵌入式 SQL 的 Python 代码组成，经过仔细注释的数据库操作程序，配有中文评论和英文评论。 https://opendatalab.org.cn/Lyra
代码 Conala来自StackOverflow问题,手动注释3k，英文 https://opendatalab.org.cn/CoNaLa/download
代码 code-alpaca ChatGPT生成20K代码指令样本 https://github.com/sahil280114/codealpaca.git
代码 32K, 四种不同类型、不同难度的代码相关中文对话数据，有大模型生成， https://github.com/zxx000728/CodeGPT
对话 LAION 策划的开放指令通用数据集中手动选择的组件子集 已开源40M 3万个,100M在路上 https://github.com/LAION-AI/Open-Instruction-Generalist
对话 Baize基于Chat GPT构建的self-chat数据 https://github.com/project-baize/baize-chatbot/tree/main/data
对话 FaceBook开源BlenderBot训练对话数据~6K https://huggingface.co/datasets/blended_skill_talk
对话 AllenAI开源38.5万个对话高质量数据集SODA https://realtoxicityprompts.apps.allenai.org/
对话 InstructDial在单一对话任务类型上进行指令微调 https://github.com/prakharguptaz/Instructdial
对话 Ultra Chat 两个独立的 ChatGPT Turbo API 进行对话，从而生成多轮对话数据 https://github.com/thunlp/UltraChat
对话 Awesome Open-domain Dialogue Models提供多个开放域对话数据 https://github.com/cingtiye/Awesome-Open-domain-Dialogue-Models#%E4%B8%AD%E6%96%87%E5%BC%80%E6%94%BE%E5%9F%9F%E5%AF%B9%E8%AF%9D%E6%95%B0%E6%8D%AE%E9%9B%86
对话 Salesforce开源超全DialogStudio https://github.com/salesforce/DialogStudio
对话 基于事实Reference的多轮问答中文数据，已开源5万，之后会开源更多 https://github.com/sufengniu/RefGPT
RLFH 北大河狸开源RLHF数据集10K，1M需要申请 https://huggingface.co/datasets/PKU-Alignment/PKU-SafeRLHF-10K
RLHF Anthropic hh-rlhf数据集 https://huggingface.co/datasets/Anthropic/hh-rlhf
RLHF Stack-exchange上问题对应多个答案，每个答案有打分 https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences/tree/main
RLHF Facebook Bot Adversarial Dialogues数据集5K https://github.com/facebookresearch/ParlAI
RLHF AllenAI Real Toxicity prompts https://github.com/facebookresearch/ParlAI
RLHF OpenAssistant Conversations 160K消息，13500人工生成, 英文为主 https://huggingface.co/datasets/OpenAssistant/oasst1
RLHF 知乎问答偏好数据集 https://huggingface.co/datasets/liyucheng/zhihu_rlhf_3k
RLHF hh-rlhf中文翻译偏好数据 https://huggingface.co/datasets/liswei/rm-static-zhTW
RLHF 面壁智能开源大规模偏好数据，基于64Kprompt使用不同模型生成4个回答使用GPT-4评估 https://github.com/OpenBMB/UltraFeedback
评估集 BigBench(Beyond the Imitation Game Benchmark) https://github.com/google/BIG-bench
评估集 Complex QA：用于ChatGPT的评测指令集 https://github.com/tan92hl/Complex-Question-Answering-Evaluation-of-ChatGPT
评估集 Langchain开源评估数据集 https://huggingface.co/LangChainDatasets
评估集 2010-2022年全国高考卷的题目 https://github.com/OpenLMLab/GAOKAO-Bench
评估集 中文通用大模型综合性评测基准SuperCLUE https://github.com/CLUEbenchmark/SuperCLUE
英文预训练 RedPajama：开源的复刻llama的预训练数据集，1.21万亿Token https://github.com/togethercomputer/RedPajama-Data
英文预训练 SlimPajama：Cerebras基于RedPajama进行清洗去重后得到的高质量数据集, 6270亿Token https://huggingface.co/datasets/cerebras/SlimPajama-627B/tree/main/train
英文预训练 The Pile：22个高质量数据集混合的预训练数据集800G,全量开放下载 https://pile.eleuther.ai/
英文预训练 Fineweb：Huggingface发布从CC清洗消重后的15T tokens web数据，超越C4，pile，pajama https://huggingface.co/datasets/HuggingFaceFW/fineweb
英文预训练 Finweb-EDU：从FineWeb中通过分类器筛选得到的高质量教育水平的数据集 5.4T Token https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu
英文预训练 1.3T高质量小规模混合预训练数据集 https://huggingface.co/datasets/Zyphra/Zyda
通用预训练 UER整理CLUECorpusSmall+News Commentary中英 https://github.com/dbiir/UER-py/wiki/%E9%A2%84%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE
中文预训练 智源人工智能开源的wudao 200G预训练数据 https://github.com/BAAI-WuDao/WuDaoMM
中文预训练 里屋社区发起开源力量收集中文互联网语料集MNBVC目标是对标ChatGPT的40T https://github.com/esbatmop/MNBVC
中文预训练 复旦开源15万中文图书下载和抽取方案 https://github.com/FudanNLPLAB/CBook-150K
中文预训练 书生万卷数据集来自公开网页多模态数据集，包括文本，图文和视频，其中文本1T，图文150G https://opendatalab.org.cn/OpenDataLab/WanJuan1_dot_0
中文预训练 昆仑天工开源3.2TB中英语料 https://github.com/SkyworkAI/Skywork
中文预训练 浪潮开源的用于Yuan1.0训练的预训练中文语料 https://www.airyuan.cn/home
领域预训练 度小满开源60G金融预训练语料 https://github.com/Duxiaoman-DI/XuanYuan
领域预训练 首个中文科学文献数据集CSL,也有多种NLP任务数据 https://github.com/ydli-ai/CSL
平行语料 news-commentary中英平行语料，用于中英间知识迁移 https://data.statmt.org/news-commentary/v15/training/
多源数据集整合 opendatalab整合了预训练阶段的多个数据源 https://opendatalab.org.cn/?industry=9821&source=JUU3JTlGJUE1JUU0JUI5JThF
Tool-搜索增强 webCPM开源的和搜索工具进行交互问答的数据集，包括网页抽取式摘要，多事实内容回答等人工标注数据 https://github.com/thunlp/WebCPM
Tool-多工具 BmTools开源的多工具调用指令数据集 https://github.com/OpenBMB/BMTools
Tool-多工具 AgentInstruct包含6项Agent任务，包括REACT式COT标注 https://thudm.github.io/AgentTuning/
Tool-多工具 MSAgent-Bench 大模型调用数据集 598k训练数据 https://modelscope.cn/datasets/damo/MSAgent-Bench/summary
Tool-多工具 MOSS开源的知识搜索，文生图，计算器，解方程等4个插件的30万条多轮对话数据 https://github.com/OpenLMLab/MOSS#%E6%95%B0%E6%8D%AE
NL2SQL DB-GPT-Hub梳理了多源text-to-sql数据集 https://github.com/eosphoros-ai/DB-GPT-Hub
长文本 清华开源的长文本对齐数据集LongAlign-10k https://huggingface.co/datasets/THUDM/LongAlign-10k
多模态-图表 MMC图表理解问答数据集 https://github.com/FuxiaoLiu/MMC
表格数据 汇总了各类表格数据 https://github.com/SpursGoZmy/Tabular-LLM

AIGC

搜索

通用搜索

  • genspark.ai: 融合了旅行，购物的真生成式搜索引擎，内容也由模型直接生成，可以说是全新搜索形式了，而模型本身回到sidebar的位置只起到辅助的作用，整个网站的风格偏小红书风格
  • 秘塔搜索: 融合了脑图，表格多模态问答的搜索应用
  • iAsk: 海外的通搜APP，支持source筛选过滤
  • You.COM : 支持多种检索增强问答模式
  • Walles.AI: 融合了图像聊天，文本聊天，chatpdf，web-copilot等多种功能的智能助手
  • webpilot.ai 比ChatGPT 自带的 Web Browsing更好用的浏览器检索插件，更适用于复杂搜索场景，也开通api调用了
  • New Bing：需要科学上网哦
  • Perplexity.ai: 同样需要科学上网，感觉比Bing做的更好的接入ChatGPT的神奇搜索引擎，在Bing之外还加入了相关推荐和追问
  • sider.ai: 支持多模型浏览器插件对话和多模态交互操作
  • 360AI搜索: 360的AI搜索和秘塔有些像
  • MyLens.AI: 支持时间轴，脑图等多种生成结果的检索增强
  • Globe Explorer:搜索query相关的知识并构建类似知识图谱的结构返回图片信息
  • 天工AI搜索：和You相同的三种模式检索增强
  • MiKU搜索：更多面向事件的搜索
  • 开搜AI搜索搜索: 免费无广告，直达结果

代码搜索

  • devv.ai: 基于微调llama2 + RAG搭建的属于程序员的搜索引擎
  • Phind: 面向开发人员的AI搜索引擎

知识管理

  • glean: 企业知识搜索和项目管理类的搜索初创公司，帮助员工快速定位信息，帮助公司整合信息
  • Mem: 个人知识管理，例如知识图谱，已获openai融资
  • GPT-Crawler: 通过简单配置，即可自行提取网页的文本信息构建知识库，并进一步自定义GPTs
  • ChatInsight: 企业级文档管理，和基于文档的对话
  • Afforai: 看到现在做的不错的个人知识管理和笔记文件，支持多文档对比，期待和zetro打通

ChatDoc

  • Kimi-Chat: 长长长长文档理解无敌的Kimi-Chat，单文档总结多文档结构化对比，无所不能，多长都行！
  • ChatDoc:ChatPDF升级版，需要科学上网，增加了表格类解析，支持选择区域的问答，在PDF识别上做的很厉害
  • AskyourPdf: 同样是上传pdf进行问答和摘要的应用
  • DocsGPT: 比较早出来的Chat DOC通用方案
  • ChatPDF: 国内的ChatPDF, 上传pdf后，会给出文章的Top5可能问题，然后对话式从文档中进行问答和检索，10s读3万字
  • AlphaBox: 从个人文件夹管理出发的文档问答工具

AI内容运营

  • Miracleplus: 全AI Agent负责运营的AI内容网站
  • goatstack: 可以自定义的论文订阅网站，每天有AI筛选并总结相关论文并推送给用户

论文研究: 日度更新，观点总结，

  • SCISPACE: 论文研究的白月光，融合了全库搜索问答，以及个人上传PDF构建知识库问答。同样支持相关论文发现，和论文划词解读。并且解读内容可以保存到notebook中方便后续查找，可以说是产品和算法强强联合了。
  • ELICIT: 和SCISPACE相似，支持一键生成论文relatied work
  • Consensus: AI加持的论文搜素，多论文总结，观点对比工具。产品排名巨高，但个人感觉搜索做的有提升空间
  • Aminer: 论文搜索，摘要，问答，搜索关键词符号化改写；但论文知识库问答有些幻觉严重
  • cool.paper: 苏神开发的基于kimi的论文阅读网站
  • OpenRead: 国内产品，面向论文写作，阅读场景，可以帮助生成文献综述，以及提供和NotionAI相似的智能Markdown用于写作
  • ChatPaper: 根据输入关键词，自动在arxiv上下载最新的论文，并对论文进行摘要总结，可以在huggingface上试用
  • researchgpt: 和ChatPDF类似，支持arivx论文下载，加载后对话式获取论文重点
  • ChatGPT-academic: 又是一个基于gradio实现的paper润色，摘要等功能打包的实现,不少功能可以借鉴
  • BriefGPT: 日更Arxiv论文，并对论文进行摘要，关键词抽取，帮助研究者了解最新动态, UI不错哟

写作效率工具类

  • AFFiNE AI: 很有创意的写作平台，结合写作绘图为一体
  • SudoWrite: 很有创意的AI卡片写作应用，主要面向各种类型的长文写作
  • 赛博马良:题如其名，可定制AI员工24小时全网抓取关注的创作选题，推送给小编进行二次创作
  • 研墨AI: 面向咨询领域的内容创作应用
  • ChatMind: chatgpt生成思维导图，模板很丰富，泛化性也不错，已经被XMind收购了
  • 范文喵写作: 范文喵写作工具，选题，大纲，写作全流程
  • WriteSonic：AI写作，支持对话和定向创作如广告文案，商品描述, 支持Web检索是亮点，支持中文
  • copy.ai: WriteSonic竞品，亮点是像论文引用一样每句话都有对应网站链接，可以一键复制到右边的创作Markdown，超级好用！
  • NotionAI：智能Markdown，适用真相！在创作中用command调用AI辅助润色，扩写，检索内容，给创意idea
  • AIEditor: NotionAI开源平替，可以替换任意模型
  • Eidos:NotionAI的离线平替，可以本地搭建笔记本并替换模型
  • Hix-AI: 同时提供copilot模式和综合写作模式
  • AI-Write： 个人使用感较好的流程化写作工具
  • Jasper: 同上，全是竞品哈哈
  • copy.down: 中文的营销文案生成，只能定向创作，支持关键词到文案的生成
  • Weaver AI: 波形智能开发的内容创作app，支持多场景写作
  • ChatExcel: 指令控制excel计算，对熟悉excel的有些鸡肋，对不熟悉的有点用
  • mindShow：免费+付费的PPT制作工具，自定义PPT模板还不够好

金融垂直领域

  • Linq:用AI简化金融分析师的研究工作
  • BrightWave:AI金融研究助手
  • Reportify: 金融领域公司公告，新闻，电话会的问答和摘要总结
  • Alpha派: kimi加持会议纪要 + 投研问答 +各类金融资讯综合的一站式平台
  • 况客FOF智能投顾:基金大模型应用，基金投顾，支持nl2sql类的数据查询，和基金信息对比查询等
  • HithinkGPT:同花顺发布金融大模型问财，覆盖查询，分析，对比，解读，预测等多个问题领域
  • FinChat.io：使用最新的财务数据，电话会议记录，季度和年度报告，投资书籍等进行训练
  • TigerGPT: 老虎证券，GPT4做个股分析，财报分析，投资知识问答
  • ChatFund：韭圈儿发布的第一个基金大模型，看起来是做了多任务指令微调，和APP已有的数据功能进行了全方位的打通，从选基，到持仓分析等等
  • ScopeChat:虚拟币应用，整个对话类似ChatLaw把工具组件嵌入了对话中
  • AInvest：个股投资，融合BI分析，广场讨论区（有演变成雪球热度指数的赶脚)
  • 无涯Infinity :星环科技发布 的金融大模型
  • 曹植:达观发布金融大模型融合data2text等金融任务，赋能报告写作
  • 妙想: 东方财富自研金融大模型开放试用,但似乎申请一直未通过
  • 恒生LightGPT:金融领域继续预训练+插件化设计
  • bondGPT: GPT4在细分债券市场的应用开放申请中
  • IndexGPT:JPMorgan在研的生成式投资顾问
  • Alpha: ChatGPT加持的金融app，支持个股信息查询，资产分析诊断，财报汇总etc
  • Composer：量化策略和AI的结合，聊天式+拖拽式投资组合构建和回测
  • Finalle.ai: 实时金融数据流接入大模型
  • OpenBB: 开源金融投资框架，OpenBB+LLamaIndex主要是大模型+API的使用方案，通过自然语言进行金融数据查询，分析和可视化

法律垂直场景

私人助理&聊天

  • Mr.-Ranedeer-: 基于prompt和GPT-4的强大能力提供个性化学习环境，个性化出题+模型解答
  • AI Topiah: 聆心智能AI角色聊天，和路飞唠了两句，多少有点中二之魂在燃烧
  • chatbase: 情感角色聊天，还没尝试
  • Vana: virtual DNA, 通过聊天创建虚拟自己！概念很炫

Agent

  • NexusGPT: AutoGPT可以出来工作了，第一个全AI Freelance平台
  • cognosys: 全网最火的web端AutoGPT，不过咋说呢试用了下感觉下巴要笑掉了，不剧透去试试你就知道
  • godmode：可以进行人为每一步交互的的AutoGPT
  • agentgpt: 基础版AutoGPT
  • AgentQL:用Query的方式和网页进行交互，开放waitlist申请了

视频拆条总结

  • Eightify: chrome插件，节省观看长视频的时间，立即获取关键**，分模块总结+时间戳摘要
  • BibiGPT: Bilibli视频内容一键总结，多模态文档

代码copilot & BI工具

  • OpenDevin:CognitionAI发布再SWE-Bench上编码能力有显著提升的智能体
  • AlphaCodium: Flow Engineering提高代码整体通过率
  • AutoDev: AI编程辅助工具
  • Codium: 开源的编程Copilot来啦
  • Copilot: 要付费哟
  • Fauxpilot: copilot本地开源替代
  • Codeium: Copilot替代品，有免费版本支持各种plugin !
  • Wolverine: 代码自我debug的python脚本
  • Screenshot-to-code: 从网页直接生成HTML代码

DB工具

  • TableAgent: 九章云极推出的数据分析，机器学习智能体
  • SwiftAgent： 数势科技推出的数据分析智能体
  • Kyligence Copilot:Kyligence发布一站式指标平台的 AI 数智助理,支持对话式指标搜索，异动归因等等
  • ai2sql: text2sql老牌公司，相比sqltranslate功能更全面，支持SQL 语法检查、格式化和生成公式
  • chat2query: text2sql 相比以上两位支持更自然的文本指令，以及更复杂的数据分析类的sql生成
  • OuterBase: text2sql 设计风格很吸睛！电子表格结合mysql和dashboard，更适合数据分析宝宝
  • Chat2DB:智能的通用数据库SQL客户端和报表工具
  • ChatBI:网易数帆发布ChatBI对话数据分析平台
  • DataHerald: Text2SQL
  • WrenAI:Text2SQL
  • Vaana: 可以本地搭载的基于python的NL2SQL+Plotly绘图框架

图片生成

  • dreamstudio.ai: 开创者，Stable Difussion， 有试用quota
  • midjourney: 开创者，艺术风格为主
  • Dall.E: 三巨头这就凑齐了
  • ControlNet: 为绘画创作加持可控性
  • gemo.ai: 多模态聊天机器人，包括文本，图像，视频生成
  • storybird: 根据提示词生成故事绘本，还可以售卖
  • Magnific.ai: 两个人的团队做出的AI图片精修师
  • Civital.com: AI图片共享网站同时支持多模型的图片生成
  • IdeoGram.ai: Google Bran研究员创立的图片生成，Md平替

视频生成

  • Morph Studio: Stability AI入场视频制作
  • 星火绘镜：星火推出，给指令自动生成电影分镜脚本，并制作分镜视频，质量比较高，但需要借助第三方软件剪辑合成
  • 即创：抖音推出，电商短视频制作，支持给定商品描述生成对应营销视频
  • 度加：支持直接text2video，但质量比星火差很多，应该说是动态PPT的视频style，但一键生成确实很像，也支持进一步编辑和素材替换
  • 一帧秒创: 支持图文转视频和数字人播报，个人体验比度加略弱
  • Elai: 支持大纲直接生成数字人播报视频，免费试用只能制作几分钟

PPT制作

  • Gamma: PPT制作神器，ProductHunt月度排名Number1
  • MindShow: 体验还不错的基于markdown直接生成PPT的软件，自定义PPT模板要付费

Resources

GPTs应用导航

Prompt和其他教程类

书籍和博客类

会议&访谈类

Papers

paper List

综述

  • A Survey of Large Language Models
  • Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing ⭐
  • Paradigm Shift in Natural Language Processing
  • Pre-Trained Models: Past, Present and Future
  • What Language Model Architecture and Pretraining objects work best for zero shot generalization ⭐
  • Towards Reasoning in Large Language Models: A Survey
  • Reasoning with Language Model Prompting: A Survey ⭐
  • An Overview on Language Models: Recent Developments and Outlook ⭐
  • A Survey of Large Language Models[6.29更新版]
  • Unifying Large Language Models and Knowledge Graphs: A Roadmap
  • Augmented Language Models: a Survey ⭐
  • Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
  • Challenges and Applications of Large Language Models
  • The Rise and Potential of Large Language Model Based Agents: A Survey
  • Large Language Models for Information Retrieval: A Survey
  • AI Alignment: A Comprehensive Survey
  • Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
  • Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
  • A Survey on Language Models for Code
  • Model-as-a-Service (MaaS): A Survey

大模型能力探究

  • In Context Learning
    • LARGER LANGUAGE MODELS DO IN-CONTEXT LEARNING DIFFERENTLY
    • How does in-context learning work? A framework for understanding the differences from traditional supervised learning
    • Why can GPT learn in-context? Language Model Secretly Perform Gradient Descent as Meta-Optimizers ⭐
    • Rethinking the Role of Demonstrations What Makes incontext learning work? ⭐
    • Trained Transformers Learn Linear Models In-Context
    • In-Context Learning Creates Task Vectors
  • 涌现能力
    • Sparks of Artificial General Intelligence: Early experiments with GPT-4
    • Emerging Ability of Large Language Models ⭐
    • LANGUAGE MODELS REPRESENT SPACE AND TIME
    • Are Emergent Abilities of Large Language Models a Mirage?
  • 能力评估
    • IS CHATGPT A GENERAL-PURPOSE NATURAL LANGUAGE PROCESSING TASK SOLVER?
    • Can Large Language Models Infer Causation from Correlation?
    • Holistic Evaluation of Language Model
    • Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
    • Theory of Mind May Have Spontaneously Emerged in Large Language Models
    • Beyond The Imitation Game: Quantifying And Extrapolating The Capabilities Of Language Models
    • Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
    • Demystifying GPT Self-Repair for Code Generation
    • Evidence of Meaning in Language Models Trained on Programs
    • Can Explanations Be Useful for Calibrating Black Box Models
    • On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
    • Language acquisition: do children and language models follow similar learning stages?
    • Language is primarily a tool for communication rather than thought
  • 领域能力
    • Capabilities of GPT-4 on Medical Challenge Problems
    • Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

Prompt Tunning范式

  • Tunning Free Prompt
    • GPT2: Language Models are Unsupervised Multitask Learners
    • GPT3: Language Models are Few-Shot Learners ⭐
    • LAMA: Language Models as Knowledge Bases?
    • AutoPrompt: Eliciting Knowledge from Language Models
  • Fix-Prompt LM Tunning
    • T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
    • PET-TC(a): Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference ⭐
    • PET-TC(b): PETSGLUE It’s Not Just Size That Matters Small Language Models are also few-shot learners
    • GenPET: Few-Shot Text Generation with Natural Language Instructions
    • LM-BFF: Making Pre-trained Language Models Better Few-shot Learners ⭐
    • ADEPT: Improving and Simplifying Pattern Exploiting Training
  • Fix-LM Prompt Tunning
    • Prefix-tuning: Optimizing continuous prompts for generation
    • Prompt-tunning: The power of scale for parameter-efficient prompt tuning ⭐
    • P-tunning: GPT Understands Too ⭐
    • WARP: Word-level Adversarial ReProgramming
  • LM + Prompt Tunning
    • P-tunning v2: Prompt Tuning Can Be Comparable to Fine-tunning Universally Across Scales and Tasks
    • PTR: Prompt Tuning with Rules for Text Classification
    • PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
  • Fix-LM Adapter Tunning
    • LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS ⭐
    • LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning
    • Parameter-Efficient Transfer Learning for NLP
    • INTRINSIC DIMENSIONALITY EXPLAINS THE EFFECTIVENESS OF LANGUAGE MODEL FINE-TUNING
    • DoRA: Weight-Decomposed Low-Rank Adaptation
  • Representation Tuning
  • ReFT: Representation Finetuning for Language Models
  • TextGrad: Automatic "Differentiation" via Text

主流LLMS和预训练

  • GLM-130B: AN OPEN BILINGUAL PRE-TRAINED MODEL
  • PaLM: Scaling Language Modeling with Pathways
  • PaLM 2 Technical Report
  • GPT-4 Technical Report
  • Backpack Language Models
  • LLaMA: Open and Efficient Foundation Language Models
  • Llama 2: Open Foundation and Fine-Tuned Chat Models
  • Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
  • OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
  • Mistral 7B
  • Ziya2: Data-centric Learning is All LLMs Need
  • MEGABLOCKS: EFFICIENT SPARSE TRAINING WITH MIXTURE-OF-EXPERTS
  • TUTEL: ADAPTIVE MIXTURE-OF-EXPERTS AT SCALE
  • Phi1- Textbooks Are All You Need ⭐
  • Phi1.5- Textbooks Are All You Need II: phi-1.5 technical report
  • Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
  • Gemini: A Family of Highly Capable Multimodal Models
  • In-Context Pretraining: Language Modeling Beyond Document Boundaries
  • LLAMA PRO: Progressive LLaMA with Block Expansion
  • QWEN TECHNICAL REPORT
  • Fewer Truncations Improve Language Modeling
  • ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools

指令微调&对齐 (instruction_tunning)

  • 经典方案
    • Flan: FINETUNED LANGUAGE MODELS ARE ZERO-SHOT LEARNERS ⭐
    • Flan-T5: Scaling Instruction-Finetuned Language Models
    • ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
    • Instruct-GPT: Training language models to follow instructions with human feedback ⭐
    • T0: MULTITASK PROMPTED TRAINING ENABLES ZERO-SHOT TASK GENERALIZATION
    • Natural Instructions: Cross-Task Generalization via Natural Language Crowdsourcing Instructions
    • Tk-INSTRUCT: SUPER-NATURALINSTRUCTIONS: Generalization via Declarative Instructions on 1600+ NLP Tasks
    • ZeroPrompt: Scaling Prompt-Based Pretraining to 1,000 Tasks Improves Zero-shot Generalization
    • Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
    • INSTRUCTEVAL Towards Holistic Evaluation of Instrucion-Tuned Large Language Models
  • SFT数据Scaling Law
    • LIMA: Less Is More for Alignment ⭐
    • Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning
    • AlpaGasus: Training A Better Alpaca with Fewer Data
    • InstructionGPT-4: A 200-Instruction Paradigm for Fine-Tuning MiniGPT-4
    • Instruction Mining: High-Quality Instruction Data Selection for Large Language Models
    • Visual Instruction Tuning with Polite Flamingo
    • Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
    • Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
    • WHEN SCALING MEETS LLM FINETUNING: THE EFFECT OF DATA, MODEL AND FINETUNING METHOD
  • 新对齐/微调方案
    • WizardLM: Empowering Large Language Models to Follow Complex Instructions ⭐
    • Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
    • Self-Alignment with Instruction Backtranslation ⭐
    • Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
    • Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks
    • PROMPT2MODEL: Generating Deployable Models from Natural Language Instructions
    • OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs
    • Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback
    • Human-like systematic generalization through a meta-learning neural network
    • Magicoder: Source Code Is All You Need
    • Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
    • Generative Representational Instruction Tuning
    • InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning Large Language Models with Instructions
    • The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
    • Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
  • 指令数据生成
    • APE: LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS ⭐
    • SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions ⭐
    • iPrompt: Explaining Data Patterns in Natural Language via Interpretable Autoprompting
    • Flipped Learning: Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
    • Fairness-guided Few-shot Prompting for Large Language Models
    • Instruction induction: From few examples to natural language task descriptions .
    • SELF-QA Unsupervised Knowledge Guided alignment.
    • GPT Self-Supervision for a Better Data Annotator
    • The Flan Collection Designing Data and Methods
    • Self-Consuming Generative Models Go MAD
    • InstructEval: Systematic Evaluation of Instruction Selection Methods
    • Overwriting Pretrained Bias with Finetuning Data
    • Improving Text Embeddings with Large Language Models
    • MAGPIE: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing
  • 如何降低通用能力损失
    • How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
    • TWO-STAGE LLM FINE-TUNING WITH LESS SPECIALIZATION AND MORE GENERALIZATION
  • 微调经验/实验报告
    • BELLE: Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases
    • Baize: Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
    • A Comparative Study between Full-Parameter and LoRA-based Fine-Tuning on Chinese Instruction Data for Large LM
    • Exploring ChatGPT’s Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences
    • Towards Better Instruction Following Language Models for Chinese: Investigating the Impact of Training Data and Evaluation
    • Fine tuning LLMs for Enterprise: Practical Guidelines and Recommendations
  • Others
    • Crosslingual Generalization through Multitask Finetuning
    • Cross-Task Generalization via Natural Language Crowdsourcing Instructions
    • UNIFIEDSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
    • PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
    • ROLELLM: BENCHMARKING, ELICITING, AND ENHANCING ROLE-PLAYING ABILITIES OF LARGE LANGUAGE MODELS

对话模型

  • LaMDA: Language Models for Dialog Applications
  • Sparrow: Improving alignment of dialogue agents via targeted human judgements ⭐
  • BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
  • How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
  • DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
  • Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
  • DiagGPT: An LLM-based Chatbot with Automatic Topic Management for Task-Oriented Dialogue

思维链 (prompt_chain_of_thought)

  • 基础&进阶用法
    • [zero-shot-COT] Large Language Models are Zero-Shot Reasoners ⭐
    • [few-shot COT] Chain of Thought Prompting Elicits Reasoning in Large Language Models ⭐
    • SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
    • LEAST-TO-MOST PROMPTING ENABLES COMPLEX REASONING IN LARGE LANGUAGE MODELS ⭐
    • Tree of Thoughts: Deliberate Problem Solving with Large Language Models
    • Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
    • Decomposed Prompting A MODULAR APPROACH FOR Solving Complex Tasks
    • Successive Prompting for Decomposing Complex Questions
    • Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
    • Beyond Chain-of-Thought, Effective Graph-of-Thought Reasoning in Large Language Models
    • Tree-of-Mixed-Thought: Combining Fast and Slow Thinking for Multi-hop Visual Reasoning
    • LAMBADA: Backward Chaining for Automated Reasoning in Natural Language
    • Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models
    • Graph of Thoughts: Solving Elaborate Problems with Large Language Models
    • Progressive-Hint Prompting Improves Reasoning in Large Language Models
    • LARGE LANGUAGE MODELS CAN LEARN RULES
    • DIVERSITY OF THOUGHT IMPROVES REASONING ABILITIES OF LARGE LANGUAGE MODELS
    • From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models
    • Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
    • LARGE LANGUAGE MODELS AS OPTIMIZERS
    • Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
    • Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
    • Abstraction-of-Thought Makes Language Models Better Reasoners
    • Faithful Logical Reasoning via Symbolic Chain-of-Thought
  • 分领域COT [Math, Code, Tabular, QA]
    • Solving Quantitative Reasoning Problems with Language Models
    • SHOW YOUR WORK: SCRATCHPADS FOR INTERMEDIATE COMPUTATION WITH LANGUAGE MODELS
    • Solving math word problems with processand outcome-based feedback
    • CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning
    • T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering
    • LEARNING PERFORMANCE-IMPROVING CODE EDITS
    • Chain of Code: Reasoning with a Language Model-Augmented Code Emulator
  • 原理分析
    • Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters ⭐
    • TEXT AND PATTERNS: FOR EFFECTIVE CHAIN OF THOUGHT IT TAKES TWO TO TANGO
    • Towards Revealing the Mystery behind Chain of Thought: a Theoretical Perspective
    • Large Language Models Can Be Easily Distracted by Irrelevant Context
    • Chain-of-Thought Reasoning Without Prompting
  • 小模型COT蒸馏
    • Specializing Smaller Language Models towards Multi-Step Reasoning ⭐
    • Teaching Small Language Models to Reason
    • Large Language Models are Reasoning Teachers
    • Distilling Reasoning Capabilities into Smaller Language Models
    • The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
  • COT样本自动构建/选择
    • STaR: Self-Taught Reasoner Bootstrapping ReasoningWith Reasoning
    • AutoCOT：AUTOMATIC CHAIN OF THOUGHT PROMPTING IN LARGE LANGUAGE MODELS
    • Large Language Models Can Self-Improve
    • Active Prompting with Chain-of-Thought for Large Language Models
    • COMPLEXITY-BASED PROMPTING FOR MULTI-STEP REASONING
  • others
    • OlaGPT Empowering LLMs With Human-like Problem-Solving abilities
    • Challenging BIG-Bench tasks and whether chain-of-thought can solve them
    • Large Language Models are Better Reasoners with Self-Verification
    • ThoughtSource A central hub for large language model reasoning data
    • Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

RLHF

  • Deepmind
    • Teaching language models to support answers with verified quotes
    • sparrow, Improving alignment of dialogue agents via targetd human judgements ⭐
    • STATISTICAL REJECTION SAMPLING IMPROVES PREFERENCE OPTIMIZATION
    • Reinforced Self-Training (ReST) for Language Modeling
    • SLiC-HF: Sequence Likelihood Calibration with Human Feedback
    • CALIBRATING SEQUENCE LIKELIHOOD IMPROVES CONDITIONAL LANGUAGE GENERATION
    • REWARD DESIGN WITH LANGUAGE MODELS
    • Final-Answer RL Solving math word problems with processand outcome-based feedback
    • Solving math word problems with process- and outcome-based feedback
    • Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models
  • openai
    • PPO: Proximal Policy Optimization Algorithms ⭐
    • Deep Reinforcement Learning for Human Preference
    • Fine-Tuning Language Models from Human Preferences
    • learning to summarize from human feedback
    • InstructGPT: Training language models to follow instructions with human feedback ⭐
    • Scaling Laws for Reward Model Over optimization ⭐
    • WEAK-TO-STRONG GENERALIZATION: ELICITING STRONG CAPABILITIES WITH WEAK SUPERVISION ⭐
    • PRM：Let's verify step by step
    • Training Verifiers to Solve Math Word Problems [PRM的前置依赖]
    • OpenAI Super Alignment Blog
    • LLM Critics Help Catch LLM Bugs
  • Anthropic
    • A General Language Assistant as a Laboratory for Alignmen
    • Red Teaming Language Models to Reduce Harms Methods,Scaling Behaviors and Lessons Learned
    • Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback ⭐
    • Constitutional AI Harmlessness from AI Feedback ⭐
    • Pretraining Language Models with Human Preferences
    • The Capacity for Moral Self-Correction in Large Language Models
    • Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Trainin
  • AllenAI, RL4LM：IS REINFORCEMENT LEARNING (NOT) FOR NATURAL LANGUAGE PROCESSING BENCHMARKS
  • 改良方案
    • RRHF: Rank Responses to Align Language Models with Human Feedback without tears
    • Chain of Hindsight Aligns Language Models with Feedback
    • AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
    • RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
    • RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
    • Training Socially Aligned Language Models in Simulated Human Society
    • RAIN: Your Language Models Can Align Themselves without Finetuning
    • Generative Judge for Evaluating Alignment
    • PEERING THROUGH PREFERENCES: UNRAVELING FEEDBACK ACQUISITION FOR ALIGNING LARGE LANGUAGE MODELS
    • SALMON: SELF-ALIGNMENT WITH PRINCIPLE-FOLLOWING REWARD MODELS
    • Large Language Model Unlearning ⭐
    • ADVERSARIAL PREFERENCE OPTIMIZATION ⭐
    • Preference Ranking Optimization for Human Alignment
    • A Long Way to Go: Investigating Length Correlations in RLHF
    • ENABLE LANGUAGE MODELS TO IMPLICITLY LEARN SELF-IMPROVEMENT FROM DATA
    • REWARD MODEL ENSEMBLES HELP MITIGATE OVEROPTIMIZATION
    • LEARNING OPTIMAL ADVANTAGE FROM PREFERENCES AND MISTAKING IT FOR REWARD
    • ULTRAFEEDBACK: BOOSTING LANGUAGE MODELS WITH HIGH-QUALITY FEEDBACK
    • MOTIF: INTRINSIC MOTIVATION FROM ARTIFICIAL INTELLIGENCE FEEDBACK
    • STABILIZING RLHF THROUGH ADVANTAGE MODEL AND SELECTIVE REHEARSAL
    • Shepherd: A Critic for Language Model Generation
    • LEARNING TO GENERATE BETTER THAN YOUR LLM
    • Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
    • Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
    • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
    • HIR The Wisdom of Hindsight Makes Language Models Better Instruction Followers
    • Aligner: Achieving Efficient Alignment through Weak-to-Strong Correction
    • A Minimaximalist Approach to Reinforcement Learning from Human Feedback
    • PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
    • Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
    • Weak-to-Strong Extrapolation Expedites Alignment
    • Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study
    • Token-level Direct Preference Optimization
    • SimPO: Simple Preference Optimization with a Reference-Free Reward
    • AUTODETECT: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
  • RL探究
    • UNDERSTANDING THE EFFECTS OF RLHF ON LLM GENERALISATION AND DIVERSITY
    • A LONG WAY TO GO: INVESTIGATING LENGTH CORRELATIONS IN RLHF
    • THE TRICKLE-DOWN IMPACT OF REWARD (IN-)CONSISTENCY ON RLHF
    • Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
    • HUMAN FEEDBACK IS NOT GOLD STANDARD
    • CONTRASTIVE POST-TRAINING LARGE LANGUAGE MODELS ON DATA CURRICULUM
    • Language Models Resist Alignment

LLM Agent 让模型使用工具 (llm_agent)

  • A Survey on Large Language Model based Autonomous Agents
  • PERSONAL LLM AGENTS: INSIGHTS AND SURVEY ABOUT THE CAPABILITY, EFFICIENCY AND SECURITY
  • 基于prompt通用方案
    • ReAct: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS ⭐
    • Self-ask: MEASURING AND NARROWING THE COMPOSITIONALITY GAP IN LANGUAGE MODELS ⭐
    • MRKL SystemsA modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
    • PAL: Program-aided Language Models
    • ART: Automatic multi-step reasoning and tool-use for large language models
    • ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models ⭐
    • Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions
    • Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models ⭐
    • Faithful Chain-of-Thought Reasoning
    • Reflexion: Language Agents with Verbal Reinforcement Learning ⭐
    • Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
    • RestGPT: Connecting Large Language Models with Real-World RESTful APIs
    • ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models
    • InstructTODS: Large Language Models for End-to-End Task-Oriented Dialogue Systems
    • TPTU: Task Planning and Tool Usage of Large Language Model-based AI Agents
    • ControlLLM: Augment Language Models with Tools by Searching on Graphs
    • Reflexion: an autonomous agent with dynamic memory and self-reflection
    • AutoAgents: A Framework for Automatic Agent Generation
    • GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension
    • PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
    • TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS ⭐ -AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
    • AIOS: LLM Agent Operating System
    • LLMCompiler An LLM Compiler for Parallel Function Calling
  • 基于微调通用方案
    • TALM: Tool Augmented Language Models
    • Toolformer: Language Models Can Teach Themselves to Use Tools ⭐
    • Tool Learning with Foundation Models
    • Tool Maker：Large Language Models as Tool Maker
    • TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
    • AgentTuning: Enabling Generalized Agent Abilities for LLMs
    • SWIFTSAGE: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
    • FireAct: Toward Language Agent Fine-tuning
    • Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
    • REST MEETS REACT: SELF-IMPROVEMENT FOR MULTI-STEP REASONING LLM AGENT
    • Efficient Tool Use with Chain-of-Abstraction Reasoning
    • Agent-FLAN: Designing Data and Methods of Effective Agent Tuning for Large Language Models
    • AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
    • Agent Lumos: Unified and Modular Training for Open-Source Language Agents
  • 调用模型方案
    • HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace
    • Gorilla：Large Language Model Connected with Massive APIs ⭐
    • OpenAGI: When LLM Meets Domain Experts
  • 垂直领域
    • 数据分析
      • DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
      • InsightLens: Discovering and Exploring Insights from Conversational Contexts in Large-Language-Model-Powered Data Analysis
      • Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
      • Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
      • TaskWeaver: A Code-First Agent Framework
      • Automated Social Science: Language Models as Scientist and Subjects
    • 金融
      • WeaverBird: Empowering Financial Decision-Making with Large Language Model, Knowledge Base, and Search Engine
      • FinGPT: Open-Source Financial Large Language Models
      • FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
      • AlphaFin：使用检索增强股票链框架对财务分析进行基准测试
      • A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist ⭐
      • Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in stock Selection
      • ENHANCING ANOMALY DETECTION IN FINANCIAL MARKETS WITH AN LLM-BASED MULTI-AGENT FRAMEWORK
      • TRADINGGPT: MULTI-AGENT SYSTEM WITH LAYERED MEMORY AND DISTINCT CHARACTERS FOR ENHANCED FINANCIAL TRADING PERFORMANCE
      • FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
      • LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction
    • 生物医疗
      • GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information
      • ChemCrow Augmenting large language models with chemistry tools
      • Generating Explanations in Medical Question-Answering by Expectation Maximization Inference over Evidence
      • Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
      • Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
    • web/mobile Agent
      • AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent
      • A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis
      • Mind2Web: Towards a Generalist Agent for the Web
      • MiniWoB++ Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
      • WEBARENA: A REALISTIC WEB ENVIRONMENT FORBUILDING AUTONOMOUS AGENTS
      • AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
      • WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
      • WebVoyager: Building an End-to-end Web Agent with Large Multimodal Models
      • CogAgent: A Visual Language Model for GUI Agents
      • Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
      • WebCanvas: Benchmarking Web Agents in Online Environments
    • 其他
      • ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models
      • WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents
      • ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
      • PointLLM: Empowering Large Language Models to Understand Point Clouds
      • Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Models
      • CarExpert: Leveraging Large Language Models for In-Car Conversational Question Answering
  • 评估
    • Evaluating Verifiability in Generative Search Engines
    • Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions
    • API-Bank: A Benchmark for Tool-Augmented LLMs
    • ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
    • Automatic Evaluation of Attribution by Large Language Models
    • Benchmarking Large Language Models in Retrieval-Augmented Generation
    • ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
  • MultiAgent
    • Generative Agents: Interactive Simulacra of Human Behavior ⭐
    • AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
    • CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society ⭐
    • Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
    • Communicative Agents for Software Development ⭐
    • METAAGENTS: SIMULATING INTERACTIONS OF HUMAN BEHAVIORS FOR LLM-BASED TASK-ORIENTED COORDINATION VIA COLLABORATIVE GENERATIVE AGENTS
    • LET MODELS SPEAK CIPHERS: MULTIAGENT DEBATE THROUGH EMBEDDINGS
    • MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning
    • War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars
    • More Agents Is All You Need
    • Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
    • Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
  • 自主学习和探索进化
    • AppAgent: Multimodal Agents as Smartphone Users
    • Investigate-Consolidate-Exploit: A General Strategy for Inter-Task Agent Self-Evolution
    • LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
    • Empowering Large Language Model Agents through Action Learning
    • Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
    • OS-COPILOT: TOWARDS GENERALIST COMPUTER AGENTS WITH SELF-IMPROVEMENT
    • LLAMA RIDER: SPURRING LARGE LANGUAGE MODELS TO EXPLORE THE OPEN WORLD
    • PAST AS A GUIDE: LEVERAGING RETROSPECTIVE LEARNING FOR PYTHON CODE COMPLETION
    • AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
    • A Survey on Self-Evolution of Large Language Models
    • ExpeL: LLM Agents Are Experiential Learners
    • ReAct Meets ActRe: When Language Agents Enjoy Training Data Autonomy
  • 其他
    • LLM+P: Empowering Large Language Models with Optimal Planning Proficiency
    • Inference with Reference: Lossless Acceleration of Large Language Models
    • RecallM: An Architecture for Temporal Context Understanding and Question Answering
    • LLaMA Rider: Spurring Large Language Models to Explore the Open World
    • LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

RAG

  • WebGPT：Browser-assisted question-answering with human feedback
  • WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences
  • WebCPM: Interactive Web Search for Chinese Long-form Question Answering ⭐
  • REPLUG: Retrieval-Augmented Black-Box Language Models ⭐
  • Query Rewriting for Retrieval-Augmented Large Language Models
  • RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
  • Atlas: Few-shot Learning with Retrieval Augmented Language Models
  • RRAML: Reinforced Retrieval Augmented Machine Learning
  • Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
  • PDFTriage: Question Answering over Long, Structured Documents
  • SELF-RAG: LEARNING TO RETRIEVE, GENERATE, AND CRITIQUE THROUGH SELF-REFLECTION ⭐
  • Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading ⭐
  • Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
  • Search-in-the-Chain: Towards Accurate, Credible and Traceable Large Language Models for Knowledge-intensive Tasks
  • Active Retrieval Augmented Generation
  • kNN-LM Does Not Improve Open-ended Text Generation
  • Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
  • Query2doc: Query Expansion with Large Language Models ⭐
  • RLCF：Aligning the Capabilities of Large Language Models with the Context of Information Retrieval via Contrastive Feedback
  • Augmented Embeddings for Custom Retrievals
  • DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries
  • Learning to Filter Context for Retrieval-Augmented Generation
  • THINK-ON-GRAPH: DEEP AND RESPONSIBLE REASON- ING OF LARGE LANGUAGE MODEL ON KNOWLEDGE GRAPH
  • RA-DIT: RETRIEVAL-AUGMENTED DUAL INSTRUCTION TUNING
  • Query Expansion by Prompting Large Language Models ⭐
  • CHAIN-OF-NOTE: ENHANCING ROBUSTNESS IN RETRIEVAL-AUGMENTED LANGUAGE MODELS
  • IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
  • T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
  • Factuality Enhanced Language Models for Open-Ended Text Generation
  • FRESHLLMS: REFRESHING LARGE LANGUAGE MODELS WITH SEARCH ENGINE AUGMENTATION
  • KwaiAgents: Generalized Information-seeking Agent System with Large Language Models
  • Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
  • Complex Claim Verification with Evidence Retrieved in the Wild
  • Retrieval-Augmented Generation for Large Language Models: A Survey
  • Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy
  • ChatQA: Building GPT-4 Level Conversational QA Models
  • RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture
  • Benchmarking Large Language Models in Retrieval-Augmented Generation
  • HyDE：Precise Zero-Shot Dense Retrieval without Relevance Labels
  • PROMPTAGATOR : FEW-SHOT DENSE RETRIEVAL FROM 8 EXAMPLES
  • SYNERGISTIC INTERPLAY BETWEEN SEARCH AND LARGE LANGUAGE MODELS FOR INFORMATION RETRIEVAL
  • T-RAG: Lessons from the LLM Trenches
  • RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation
  • ARAGOG: Advanced RAG Output Grading
  • ActiveRAG: Revealing the Treasures of Knowledge via Active Learning
  • RAFT: Adapting Language Model to Domain Specific RAG
  • ASK THE RIGHT QUESTIONS:ACTIVE QUESTION REFORMULATION WITH REINFORCEMENT LEARNING [传统方案参考]
  • Query Expansion Techniques for Information Retrieval a Survey [传统方案参考]
  • Learning to Rewrite Queries [传统方案参考]
  • Managing Diversity in Airbnb Search[传统方案参考]
  • 新向量模型用于Recall和Ranking
    • BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
    • 网易为RAG设计的BCE Embedding技术报告
    • BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models
    • D2LLM: Decomposed and Distilled Large Language Models for Semantic Search
  • Contextual.ai-RAG2.0
  • When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively
  • Ranking Manipulation for Conversational Search Engines
  • PlanRAG: A Plan-then-Retrieval Augmented Generation for Generative Large Language Models as Decision Makers
  • Self-Knowledge Guided Retrieval Augmentation for Large Language Models
  • Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs
  • Self-DC: When to retrieve and When to generate Self Divide-and-Conquer for Compositional Unknown Questions

大模型图表理解和生成

  • survey
    • Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
    • Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey
    • Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
  • prompt
    • Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning
    • Tab-CoT: Zero-shot Tabular Chain of Thought
    • Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
  • fintuning
    • TableLlama: Towards Open Large Generalist Models for Tables
    • TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
  • multimodal
    • MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning
    • ChartLlama: A Multimodal LLM for Chart Understanding and Generation
    • ChartAssisstant: A Universal Chart Multimodal Language Model via Chart-to-Table Pre-training and Multitask Instruction Tuning
    • ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
    • ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
    • MATCHA : Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
    • UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning
    • TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
    • Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
    • TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains
    • TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy

LLM+KG

  • 综述类
  • KG用于大模型推理
    • Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs
    • MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Models
    • Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
    • Domain Specific Question Answering Over Knowledge Graphs Using Logical Programming and Large Language Models
    • BRING YOUR OWN KG: Self-Supervised Program Synthesis for Zero-Shot KGQA
    • StructGPT: A General Framework for Large Language Model to Reason over Structured Data
  • 大模型用于KG构建
    • Enhancing Knowledge Graph Construction Using Large Language Models
    • LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT
    • ITERATIVE ZERO-SHOT LLM PROMPTING FOR KNOWLEDGE GRAPH CONSTRUCTION
    • Exploring Large Language Models for Knowledge Graph Completion

Humanoid Agents

  • HABITAT 3.0: A CO-HABITAT FOR HUMANS, AVATARS AND ROBOTS
  • Humanoid Agents: Platform for Simulating Human-like Generative Agents
  • Voyager: An Open-Ended Embodied Agent with Large Language Models
  • Shaping the future of advanced robotics
  • AUTORT: EMBODIED FOUNDATION MODELS FOR LARGE SCALE ORCHESTRATION OF ROBOTIC AGENTS
  • ROBOTIC TASK GENERALIZATION VIA HINDSIGHT TRAJECTORY SKETCHES
  • ALFWORLD: ALIGNING TEXT AND EMBODIED ENVIRONMENTS FOR INTERACTIVE LEARNING
  • MINEDOJO: Building Open-Ended Embodied Agents with Internet-Scale Knowledge
  • LEGENT: Open Platform for Embodied Agents

pretrain_data & pretrain

  • DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining
  • The Pile: An 800GB Dataset of Diverse Text for Language Modeling
  • CCNet: Extracting High Quality Monolingual Datasets fromWeb Crawl Data
  • WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
  • CLUECorpus2020: A Large-scale Chinese Corpus for Pre-training Language Model
  • In-Context Pretraining: Language Modeling Beyond Document Boundaries
  • Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
  • Zyda: A 1.3T Dataset for Open Language Modeling
  • Entropy Law: The Story Behind Data Compression and LLM Performance
  • Data, Data Everywhere: A Guide for Pretraining Dataset Construction
  • Data curation via joint example selection further accelerates multimodal learning

领域模型SFT(domain_llms)

  • 金融
    • BloombergGPT： A Large Language Model for Finance
    • FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
    • CFGPT: Chinese Financial Assistant with Large Language Model
    • CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
    • InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning
    • BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark
    • PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
    • The FinBen: An Holistic Financial Benchmark for Large Language Models
    • XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters
  • 生物医疗
    • MedGPT: Medical Concept Prediction from Clinical Narratives
    • BioGPT：Generative Pre-trained Transformer for Biomedical Text Generation and Mining
    • PubMed GPT: A Domain-specific large language model for biomedical text ⭐
    • ChatDoctor：Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
    • Med-PaLM：Large Language Models Encode Clinical Knowledge[V1,V2] ⭐
    • SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support
    • Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
  • 其他
    • Galactia：A Large Language Model for Science
    • Augmented Large Language Models with Parametric Knowledge Guiding
    • ChatLaw Open-Source Legal Large Language Model ⭐
    • MediaGPT : A Large Language Model For Chinese Media
    • KITLM: Domain-Specific Knowledge InTegration into Language Models for Question Answering
    • EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
    • TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
    • LLEMMA: AN OPEN LANGUAGE MODEL FOR MATHEMATICS
    • MEDITAB: SCALING MEDICAL TABULAR DATA PREDICTORS VIA DATA CONSOLIDATION, ENRICHMENT, AND REFINEMENT
    • PLLaMa: An Open-source Large Language Model for Plant Science
    • ADAPTING LARGE LANGUAGE MODELS VIA READING COMPREHENSION

LLM超长文本处理 (long_input)

  • 位置编码、注意力机制优化
  • 上文压缩排序方案
    • Lost in the Middle: How Language Models Use Long Contexts ⭐
    • LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models
    • LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression ⭐
    • Learning to Compress Prompts with Gist Tokens
    • Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering
    • LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
    • PCToolkit: A Unified Plug-and-Play Prompt Compression Toolkit of Large Language Models
    • Are Long-LLMs A Necessity For Long-Context Tasks?
  • 训练和模型架构方案
    • Never Train from Scratch: FAIR COMPARISON OF LONGSEQUENCE MODELS REQUIRES DATA-DRIVEN PRIORS
    • Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
    • Never Lost in the Middle: Improving Large Language Models via Attention Strengthening Question Answering
    • Focused Transformer: Contrastive Training for Context Scaling
    • Effective Long-Context Scaling of Foundation Models
    • ON THE LONG RANGE ABILITIES OF TRANSFORMERS
    • Efficient Long-Range Transformers: You Need to Attend More, but Not Necessarily at Every Layer
    • POSE: EFFICIENT CONTEXT WINDOW EXTENSION OF LLMS VIA POSITIONAL SKIP-WISE TRAINING
    • LONGLORA: EFFICIENT FINE-TUNING OF LONGCONTEXT LARGE LANGUAGE MODELS
    • LongAlign: A Recipe for Long Context Alignment of Large Language Models
    • Data Engineering for Scaling Language Models to 128K Context
    • MEGALODON: Efficient LLM Pretraining and Inference with Unlimited Context Length
    • Make Your LLM Fully Utilize the Context
  • 效率优化
    • Efficient Attention: Attention with Linear Complexities
    • Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
    • HyperAttention: Long-context Attention in Near-Linear Time
    • FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
    • With Greater Text Comes Greater Necessity: Inference-Time Training Helps Long Text Generation

LLM长文本生成（long_output）

  • Re3 : Generating Longer Stories With Recursive Reprompting and Revision
  • RECURRENTGPT: Interactive Generation of (Arbitrarily) Long Text
  • DOC: Improving Long Story Coherence With Detailed Outline Control
  • Weaver: Foundation Models for Creative Writing
  • Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models

NL2SQL

  • 大模型方案
    • DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction ⭐
    • C3: Zero-shot Text-to-SQL with ChatGPT ⭐
    • SQL-PALM: IMPROVED LARGE LANGUAGE MODEL ADAPTATION FOR TEXT-TO-SQL
    • BIRD Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQL ⭐
    • A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL
    • ChatDB: AUGMENTING LLMS WITH DATABASES AS THEIR SYMBOLIC MEMORY
    • A comprehensive evaluation of ChatGPT’s zero-shot Text-to-SQL capability
    • Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning
  • Domain Knowledge Intensive
    • Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
    • Bridging the Generalization Gap in Text-to-SQL Parsing with Schema Expansion
    • Towards Robustness of Text-to-SQL Models against Synonym Substitution
    • FinQA: A Dataset of Numerical Reasoning over Financial Data
  • others
    • RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
    • MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL

Code Generation

  • Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering
  • Codeforces as an Educational Platform for Learning Programming in Digitalization
  • Competition-Level Code Generation with AlphaCode
  • CODECHAIN: TOWARDS MODULAR CODE GENERATION THROUGH CHAIN OF SELF-REVISIONS WITH REPRESENTATIVE SUB-MODULES
  • AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation

降低模型幻觉 (reliability)

  • Survey
    • Large language models and the perils of their hallucinations
    • Survey of Hallucination in Natural Language Generation
    • Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
    • A Survey of Hallucination in Large Foundation Models
    • A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
    • Calibrated Language Models Must Hallucinate
    • Why Does ChatGPT Fall Short in Providing Truthful Answers?
  • Prompt or Tunning
    • R-Tuning: Teaching Large Language Models to Refuse Unknown Questions
    • PROMPTING GPT-3 TO BE RELIABLE
    • ASK ME ANYTHING: A SIMPLE STRATEGY FOR PROMPTING LANGUAGE MODELS ⭐
    • On the Advance of Making Language Models Better Reasoners
    • RefGPT: Reference → Truthful & Customized Dialogues Generation by GPTs and for GPTs
    • Rethinking with Retrieval: Faithful Large Language Model Inference
    • GENERATE RATHER THAN RETRIEVE: LARGE LANGUAGE MODELS ARE STRONG CONTEXT GENERATORS
    • Large Language Models Struggle to Learn Long-Tail Knowledge
  • Decoding Strategy
    • Trusting Your Evidence: Hallucinate Less with Context-aware Decoding ⭐
    • SELF-REFINE:ITERATIVE REFINEMENT WITH SELF-FEEDBACK ⭐
    • Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
    • Inference-Time Intervention: Eliciting Truthful Answers from a Language Model
    • Enabling Large Language Models to Generate Text with Citations
    • Factuality Enhanced Language Models for Open-Ended Text Generation
    • KL-Divergence Guided Temperature Sampling
    • KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection
    • CONTRASTIVE DECODING IMPROVES REASONING IN LARGE LANGUAGE MODEL
    • Contrastive Decoding: Open-ended Text Generation as Optimization
  • Probing and Detection
    • Automatic Evaluation of Attribution by Large Language Models
    • QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
    • Zero-Resource Hallucination Prevention for Large Language Models
    • LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
    • Language Models (Mostly) Know What They Know ⭐
    • LM vs LM: Detecting Factual Errors via Cross Examination
    • Do Language Models Know When They’re Hallucinating References?
    • SELFCHECKGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
    • SELF-CONTRADICTORY HALLUCINATIONS OF LLMS: EVALUATION, DETECTION AND MITIGATION
    • Self-consistency for open-ended generations
    • Improving Factuality and Reasoning in Language Models through Multiagent Debate
    • Selective-LAMA: Selective Prediction for Confidence-Aware Evaluation of Language Models
    • Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
  • Reviewing and Calibration
    • Truth-o-meter: Collaborating with llm in fighting its hallucinations
    • RARR: Researching and Revising What Language Models Say, Using Language Models
    • CRITIC: LARGE LANGUAGE MODELS CAN SELFCORRECT WITH TOOL-INTERACTIVE CRITIQUING
    • VALIDATING LARGE LANGUAGE MODELS WITH RELM
    • PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
    • Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback
    • Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes
    • Woodpecker: Hallucination Correction for Multimodal Large Language Models
    • Zero-shot Faithful Factual Error Correction

大模型评估（evaluation）

  • 事实性评估
    • TRUSTWORTHY LLMS: A SURVEY AND GUIDELINE FOR EVALUATING LARGE LANGUAGE MODELS’ ALIGNMENT
    • TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
    • TRUE: Re-evaluating Factual Consistency Evaluation
    • FACTSCORE: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
    • KoLA: Carefully Benchmarking World Knowledge of Large Language Models
    • When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
    • FACTOOL: Factuality Detection in Generative AI A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
    • LONG-FORM FACTUALITY IN LARGE LANGUAGE MODELS
  • 检测任务
    • Detecting Pretraining Data from Large Language Models
    • Scalable Extraction of Training Data from (Production) Language Models
    • Rethinking Benchmark and Contamination for Language Models with Rephrased Samples

推理优化(inference)

  • Fast Transformer Decoding: One Write-Head is All You Need
  • Fast Inference from Transformers via Speculative Decoding
  • GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
  • Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
  • SkipDecode: Autoregressive Skip Decoding with Batching and Caching for Efficient LLM Inference
  • BatchPrompt: Accomplish more with less

模型知识编辑黑科技(model_edit)

  • ROME：Locating and Editing Factual Associations in GPT
  • Transformer Feed-Forward Layers Are Key-Value Memories
  • MEMIT: Mass-Editing Memory in a Transformer
  • MEND：Fast Model Editing at Scale
  • Editing Large Language Models: Problems, Methods, and Opportunities
  • Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

模型合并和剪枝(model_merge)

  • Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
  • DARE Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
  • EDITING MODELS WITH TASK ARITHMETIC
  • TIES-Merging: Resolving Interference When Merging Models
  • LM-Cocktail: Resilient Tuning of Language Models via Model Merging
  • SLICEGPT: COMPRESS LARGE LANGUAGE MODELS BY DELETING ROWS AND COLUMNS
  • Checkpoint Merging via Bayesian Optimization in LLM Pretrainin
  • Arcee's MergeKit: A Toolkit for Merging Large Language Models

MOE

  • Tricks for Training Sparse Translation Models
  • ST-MoE: Designing Stable and Transferable Sparse Expert Models
  • Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
  • GLaM: Efficient Scaling of Language Models with Mixture-of-Experts
  • GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
  • OUTRAGEOUSLY LARGE NEURAL NETWORKS: THE SPARSELY-GATED MIXTURE-OF-EXPERTS LAYER
  • DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale
  • Dense-to-Sparse Gate for Mixture-of-Experts
  • Efficient Large Scale Language Modeling with Mixtures of Experts

Other Prompt Engineer(prompt_engineer)

  • Calibrate Before Use: Improving Few-Shot Performance of Language Models
  • In-Context Instruction Learning
  • LEARNING PERFORMANCE-IMPROVING CODE EDITS
  • Boosting Theory-of-Mind Performance in Large Language Models via Prompting
  • Generated Knowledge Prompting for Commonsense Reasoning
  • RECITATION-AUGMENTED LANGUAGE MODELS
  • kNN PROMPTING: BEYOND-CONTEXT LEARNING WITH CALIBRATION-FREE NEAREST NEIGHBOR INFERENCE
  • EmotionPrompt: Leveraging Psychology for Large Language Models Enhancement via Emotional Stimulus
  • Causality-aware Concept Extraction based on Knowledge-guided Prompting
  • LARGE LANGUAGE MODELS AS OPTIMIZERS
  • Prompts As Programs: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization
  • Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
  • RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions
  • MedPrompt: Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
  • DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
  • Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
  • In-Context Learning for Extreme Multi-Label Classification
  • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
  • DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
  • CONNECTING LARGE LANGUAGE MODELS WITH EVOLUTIONARY ALGORITHMS YIELDS POWERFUL PROMP OPTIMIZERS

Multimodal

  • InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
  • Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
  • LLava Visual Instruction Tuning
  • MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
  • BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions
  • mPLUG-Owl : Modularization Empowers Large Language Models with Multimodality
  • LVLM eHub: A Comprehensive Evaluation Benchmark for Large VisionLanguage Models
  • Mirasol3B: A Multimodal Autoregressive model for time-aligned and contextual modalities
  • PaLM-E: An Embodied Multimodal Language Model
  • TabLLM: Few-shot Classification of Tabular Data with Large Language Models
  • AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
  • Sora tech report
  • Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study
  • OCR
    • Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models
    • Large OCR Model:An Empirical Study of Scaling Law for OCR
    • ON THE HIDDEN MYSTERY OF OCR IN LARGE MULTIMODAL MODELS
  • PreFLMR: Scaling Up Fine-Grained Late-Interaction Multi-modal Retrievers
  • Many-Shot In-Context Learning in Multimodal Foundation Models
  • Adding Conditional Control to Text-to-Image Diffusion Models

Timeseries LLM

  • TimeGPT-1
  • Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
  • TIME-LLM: TIME SERIES FORECASTING BY REPROGRAMMING LARGE LANGUAGE MODELS
  • Large Language Models Are Zero-Shot Time Series Forecasters
  • TEMPO: PROMPT-BASED GENERATIVE PRE-TRAINED TRANSFORMER FOR TIME SERIES FORECASTING
  • Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing
  • Lag-Llama: Towards Foundation Models for Time Series Forecasting
  • PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting

Quanization

  • AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
  • LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
  • LLM.int8() 8-bit Matrix Multiplication for Transformers at Scale
  • SmoothQuant Accurate and Efficient Post-Training Quantization for Large Language Models

Adversarial Attacking

  • Curiosity-driven Red-teaming for Large Language Models
  • Red Teaming Language Models with Language Models
  • EXPLORE, ESTABLISH, EXPLOIT: RED-TEAMING LANGUAGE MODELS FROM SCRATCH

Others

  • Pretraining on the Test Set Is All You Need 哈哈作者你是懂讽刺文学的
  • Learnware: Small Models Do Big
  • The economic potential of generative AI
  • A PhD Student’s Perspective on Research in NLP in the Era of Very Large Language Models

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感谢作者

作者真棒！期待实时更新，以及增加更多模型评论和模型选择方面的内容，感谢🌹🌹🌹

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