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

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Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

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LLM "Reasoning" is a Mirage? It Struggles with New Problems!

This study investigated Chain-of-Thought reasoning in LLMs using a controlled environment, revealing its limitations in handling novel tasks, lengths, and formats. This implies that the apparent "reasoning" may be due to memorization rather than logical inference, emphasizing the need for more robust reasoning models.

Possible Conflicts of Interest

None identified.

Identified Weaknesses

Synthetic dataset

The training and testing process is conducted on a synthetic dataset that may not entirely reflect real-world scenarios.

Potential for coincidental results

The model's performance on certain aspects of reasoning, such as element and length generalization, may be influenced by factors like random coincidences in generated output.

Choice of LLM

The reliance on GPT-2 as the language model may limit the generalizability of findings to larger, more powerful LLMs.

Simplified reasoning

The simplification of the reasoning process and the definition of 'generalization' using simple transformations on atomic elements may not capture the full complexity of reasoning seen in more complex real-world scenarios.

Focus on decoder-only LLMs

The study primarily focuses on decoder-only LLMs, potentially overlooking specific characteristics of encoder-decoder models.

Rating Explanation

This paper offers valuable insights into the limitations of chain-of-thought prompting and introduces a novel framework for systematic analysis. The controlled environment and the focus on distributional shifts provide a solid foundation for understanding the generalization capabilities of LLMs.

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