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Who cites «Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance»

55 papers of our library cite it (of the 141,840 whose bibliographies we read) · 12 with a page here, the most cited first · ← back to «Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance»

  1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,863 citations
  2. Scaling Relationship on Learning Mathematical Reasoning with Large Language ModelsarXiv:2308.01825Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.2023 · 252 citations
  3. Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' AlignmentarXiv:2308.05374Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li2023 · 220 citations
  4. Fine-Grained Human Feedback Gives Better Rewards for Language Model TrainingarXiv:2306.01693Zeqiu Wu, Yushi Hu, Weijia Shi, et al.2023 · 212 citations
  5. LawBench: Benchmarking Legal Knowledge of Large Language ModelsarXiv:2309.16289Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.2023 · 186 citations
  6. SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language ModelsarXiv:2307.10635Xiaoxuan Wang, Ziniu Hu, Pan Lu, et al.2023 · 149 citations
  7. Aligning Language Models to User OpinionsFindings of the Association for Computational LinguisticsEunJeong Hwang, Bodhisattwa Majumder, Niket Tandon2023 · 90 citations
  8. Survey of Vulnerabilities in Large Language Models Revealed by Adversarial AttacksarXiv:2310.10844Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.2023 · 83 citations
  9. Contrastive Decoding Improves Reasoning in Large Language ModelsarXiv:2309.09117Sean O'Brien, Mike Lewis2023 · 67 citations

43 more papers of our library cite it; they have no page here yet.

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