Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?

• URL: http://arxiv.org/abs/2410.23856v1
• Date: Thu, 31 Oct 2024 12:07:44 GMT
• Title: Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?
• Authors: Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo, Zengmao Wang, Bo Han,
• Abstract summary: This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales. We construct NoRa dataset that is tailored to evaluate the robustness of reasoning in the presence of noisy rationales. We propose the method of contrastive denoising with noisy chain-of-thought (CD-CoT)
• Score: 19.13886382791074
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• Abstract: This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales, which include irrelevant or inaccurate reasoning thoughts within examples used for in-context learning. We construct NoRa dataset that is tailored to evaluate the robustness of reasoning in the presence of noisy rationales. Our findings on NoRa dataset reveal a prevalent vulnerability to such noise among current LLMs, with existing robust methods like self-correction and self-consistency showing limited efficacy. Notably, compared to prompting with clean rationales, base LLM drops by 1.4%-19.8% in accuracy with irrelevant thoughts and more drastically by 2.2%-40.4% with inaccurate thoughts. Addressing this challenge necessitates external supervision that should be accessible in practice. Here, we propose the method of contrastive denoising with noisy chain-of-thought (CD-CoT). It enhances LLMs' denoising-reasoning capabilities by contrasting noisy rationales with only one clean rationale, which can be the minimal requirement for denoising-purpose prompting. This method follows a principle of exploration and exploitation: (1) rephrasing and selecting rationales in the input space to achieve explicit denoising and (2) exploring diverse reasoning paths and voting on answers in the output space. Empirically, CD-CoT demonstrates an average improvement of 17.8% in accuracy over the base model and shows significantly stronger denoising capabilities than baseline methods. The source code is publicly available at: https://github.com/tmlr-group/NoisyRationales.

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