yu8970 / arxivqa

WIP - Automated Question Answering for ArXiv Papers with Large Language Models

Automated Question Answering with ArXiv Papers

Latest 25 Papers

• JudgeLM: Fine-tuned Large Language Models are Scalable Judges - [Arxiv] [QA]
• Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time - [Arxiv] [QA]
• HyperFields: Towards Zero-Shot Generation of NeRFs from Text - [Arxiv] [QA]
• Controlled Decoding from Language Models - [Arxiv] [QA]
• LLM-FP4: 4-Bit Floating-Point Quantized Transformers - [Arxiv] [QA]
• LightSpeed: Light and Fast Neural Light Fields on Mobile Devices - [Arxiv] [QA]
• TD-MPC2: Scalable, Robust World Models for Continuous Control - [Arxiv] [QA]
• CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images - [Arxiv] [QA]
• DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior - [Arxiv] [QA]
• QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models - [Arxiv] [QA]
• Detecting Pretraining Data from Large Language Models - [Arxiv] [QA]
• ConvNets Match Vision Transformers at Scale - [Arxiv] [QA]
• A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation - [Arxiv] [QA]
• An Early Evaluation of GPT-4V(ision) - [Arxiv] [QA]
• CLEX: Continuous Length Extrapolation for Large Language Models - [Arxiv] [QA]
• TiC-CLIP: Continual Training of CLIP Models - [Arxiv] [QA]
• Woodpecker: Hallucination Correction for Multimodal Large Language Models - [Arxiv] [QA]
• What's Left? Concept Grounding with Logic-Enhanced Foundation Models - [Arxiv] [QA]
• Dissecting In-Context Learning of Translations in GPTs - [Arxiv] [QA]
• In-Context Learning Creates Task Vectors - [Arxiv] [QA]
• KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval - [Arxiv] [QA]
• TRAMS: Training-free Memory Selection for Long-range Language Modeling - [Arxiv] [QA]
• Moral Foundations of Large Language Models - [Arxiv] [QA]
• SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding - [Arxiv] [QA]
• RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions - [Arxiv] [QA]

List of Papers by Year

• Papers for 2023
• Papers for 2022
• Papers for 2021
• Papers for 2020
• Papers for 2019
• Papers for 2018
• Papers for 2017
• Papers for 2016
• Papers for 2015
• Papers for 2014
• Papers for 2013
• Papers for 2012
• Papers for 2010
• Papers for 2009

Acknowledgements

This project is made possible through the generous support of Anthropic, who provided free access to the Claude-2.0 API.