LLMs with Chain-of-Thought Are Non-Causal Reasoners
Guangsheng Bao Hongbo Zhang Linyi Yang Cunxiang Wang Yue Zhang
This paper explores the role of the Chain of Thought (CoT) in Large Language
Models (LLMs) reasoning. Despite its potential to improve task performance, our
analysis reveals a surprising frequency of correct answers following incorrect
CoTs and vice versa. We employ causal analysis to assess the cause-effect
relationship between CoTs/instructions and answers in LLMs, uncovering the
Structural Causal Model (SCM) that LLMs approximate. By comparing the implied
SCM with that of human reasoning, we highlight discrepancies between LLM and
human reasoning processes. We further examine the factors influencing the
causal structure of the implied SCM, revealing that in-context learning,
supervised fine-tuning, and reinforcement learning on human feedback
significantly impact the causal relations. We release the code and results at
https://github.com/StevenZHB/CoT_Causal_Analysis.