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Although Chain-of-Thought (CoT) has achieved remarkable success in enhancing the reasoning ability of large language models (LLMs), the mechanism of CoT remains a ``black box''.
Randomization analysis of experimental data: The fisher randomization test comment
Donald B Rubin · 1980
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Disco: Distilling counterfactuals with large language models
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Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, and Huan Liu · 2024
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Unlocking the capabilities of thought: A reasoning boundary framework to quantify and optimize chain-of-thought
Qiguang Chen, Libo Qin, Jiaqi Wang, Jinxuan Zhou, and Wanxiang Che · 2024
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Navigate through enigmatic labyrinth a survey of chain of thought reasoning: Advances, frontiers and future
Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Tao He, Haotian Wang, Weihua Peng, Ming Liu, Bing Qin, and Ting Liu · 2024
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Towards revealing the mystery behind chain of thought: a theoretical perspective
Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang · 2024
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Omni-math: A universal olympiad level mathematic benchmark for large language models
Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, et al · 2024
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Chain-of-thought prompting elicits reasoning in large language models
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Tree-planner: Efficient close-loop task planning with large language models
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Cladder: assessing causal reasoning in language models
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Causal reasoning and large language models: Opening a new frontier for causality
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To cot or not to cot? chain-of-thought helps mainly on math and symbolic reasoning
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Causal prompting: Debiasing large language model prompting based on front-door adjustment
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Causal distillation for alleviating performance heterogeneity in recommender systems, 2024
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Interventional fairness on partially known causal graphs: A constrained optimization approach
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Towards system 2 reasoning in llms: Learning how to think with meta chain-of-though
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