Chain-of-Thought Reasoning Without Prompting

Published on Feb 15
· Featured in Daily Papers on Feb 16


In enhancing the reasoning capabilities of large language models (LLMs), prior research primarily focuses on specific prompting techniques such as few-shot or zero-shot chain-of-thought (CoT) prompting. These methods, while effective, often involve manually intensive prompt engineering. Our study takes a novel approach by asking: Can LLMs reason effectively without prompting? Our findings reveal that, intriguingly, CoT reasoning paths can be elicited from pre-trained LLMs by simply altering the decoding process. Rather than conventional greedy decoding, we investigate the top-k alternative tokens, uncovering that CoT paths are frequently inherent in these sequences. This approach not only bypasses the confounders of prompting but also allows us to assess the LLMs' intrinsic reasoning abilities. Moreover, we observe that the presence of a CoT in the decoding path correlates with a higher confidence in the model's decoded answer. This confidence metric effectively differentiates between CoT and non-CoT paths. Extensive empirical studies on various reasoning benchmarks show that the proposed CoT-decoding substantially outperforms the standard greedy decoding.


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Honestly I was a bit confused reading this. How do they identify CoT paths?

Interestingly, upon examining the model’s logits, we found that the presence of a CoT path typically
leads to a more confident decoding of the final answer, characterized by a significant probability
disparity between the top and secondary tokens

"typically" is a neat finding but not very prescriptive.

I could be wrong here, but it looked like beam search with some cool findings. Its neat but marketed as something different.

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