Chain-of-Thought Reasoning Without Prompting
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.
65cedd98d108e608cabc6d9e
Xuezhi Wang
65cedd98d108e608cabc6d9f
Denny Zhou
claimed_verified
2024-02-20T18:25:59.979Z
65d4d62d493aeaf0bb295f1b
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
Denny Zhou
user
dennyzhou
Chain-of-Thought Reasoning Without Prompting
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.
LLMs can perform chain-of-thought reasoning through top-k decoding without manual prompt engineering, outperforming greedy decoding and showing higher confidence in answers.
large language models (LLMs) chain-of-thought (CoT) prompting few-shot zero-shot manual prompt engineering decoding process top-k alternative tokens intrinsic reasoning abilities confidence metric reasoning benchmarks greedy decoding