Deciphering the Factors Influencing the Efficacy of Chain-of-Thought: Probability, Memorization, and Noisy Reasoning

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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LLMs Think in Probabilities, Memories, and Noise (But Also Kinda Reason)

Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting exhibit a blend of noisy reasoning, probability matching based on output likelihood, and memorization. LLM performance isn't pure symbolic reasoning, but it improves substantially with CoT, suggesting a more nuanced process than simple memorization.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Limited task scope

The study focuses solely on shift ciphers, which simplifies the problem compared to real-world reasoning tasks. While useful for isolating factors in a controlled way, its conclusions cannot be generalized to more complex scenarios.

Unfaithful explanations and reliance on self-conditioning

The models studied exhibit unfaithfulness between reasoning steps and final answers, revealing a reliance on memorization rather than pure reasoning. The success of CoT prompting seems tied to outputting helpful text for the model to condition on, suggesting an external rather than internal reasoning process.

Multiple examples in one demonstration

The prompt design contains multiple examples in a single demonstration (one-shot CoT), making it less clear how much of the effect of CoT can be attributed to a single demonstration.

Rating Explanation

This paper presents a strong, focused analysis of LLM reasoning using a clever task (shift ciphers). The methodology isolates key factors and provides quantitative evidence. While limited in scope to a single task, the findings about probabilistic, memorization-influenced noisy reasoning are valuable. No apparent conflicts of interest were found.

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