Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens

In this episode, we discuss Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens by Chengshuai Zhao, Zhen Tan, Pingchuan Ma, Dawei Li, Bohan Jiang, Yancheng Wang, Yingzhen Yang, Huan Liu. The paper investigates Chain-of-Thought (CoT) reasoning in large language models, revealing it may not reflect true inferential processes but rather learned patterns tied to training data distributions. Using a controlled environment called DataAlchemy, the authors show CoT reasoning breaks down when models face out-of-distribution tasks, lengths, or formats. This highlights the limitations of CoT prompting and the challenge of achieving authentic, generalizable reasoning in LLMs.

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