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The papers are organized according to our survey: Evaluating Large Language Models: A Comprehensive Survey.
In terms of this benchmark...
Hi TJUNLP team, thanks so much for the great work! I think a paper that presents a holistic view of current NLP benchmarks is relevant amidst the many ongoing efforts.
To this end, I'd like to point out a couple works concerning evaluating language models on the coding related tasks, such as completion, patch generation, and language agents using code as actions.
Thanks in advance!
Hi,
Please note our recent paper How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective. on this topic :)
Cheers
Hi,
I have read your insightful paper and found it to be a valuable contribution to the field.
I would like to kindly suggest adding our recent work to your survey.
📄 Paper : Ask Again, Then Fail: Large Language Models' Vacillations in Judgement
This paper uncovers that the judgement consistency of LLM dramatically decreases when confronted with disruptions like questioning, negation, or misleading, even though its previous judgments were correct. It also explores several prompting methods to mitigate this issue and demonstrates their effectiveness.
Thank you for your consideration! :)
Hi all,
this is an amazing resource! Thanks for sharing openly! I was just wondering under which conditions one could reuse the materials provided here. Would you mind adding a license file? If you're new to licensing and/or wonder which license to use, you can read more in this blob post: https://focalplane.biologists.com/2023/05/06/if-you-license-it-itll-be-harder-to-steal-it-why-we-should-license-our-work/
Thanks!
Best,
Robert
in 📚Knowledge and Capability Evaluation -> Question Answering
RAGAS: Automated Evaluation of Retrieval Augmented Generation
paper
RAGAS framework on github
Check http://openeval.org.cn/rank and click on 知识能力
(intellectual ability) option, then the leaderboard does not show up:
http://openeval.org.cn/doc?id=2
I cannot find any further explanation of the chosen metrics.
I can find it nowhere...
Please note our paper on evaluation, which could be an important building block for multilingual evaluation and cultural understanding.
SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization. PandaLM is the first to evaluate llm using a finetuned llm.
Where are the results of these two benchmarks for domain evaluation in the OpenEval leaderboard?
Hi there,
Thanks for the effort in putting up this repo on LLMs evaluation.
I'd like to suggest adding our work, SpyGame, a framework for evaluating language model intelligence. We propose to use word guessing games to assess the language and theory of mind intelligences of LLMs.
Paper: Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models
GitHub: https://github.com/Skytliang/SpyGame
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