Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias

• URL: http://arxiv.org/abs/2407.15366v1
• Date: Mon, 22 Jul 2024 04:25:01 GMT
• Title: Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias
• Authors: Rongwu Xu, Zi'an Zhou, Tianwei Zhang, Zehan Qi, Su Yao, Ke Xu, Wei Xu, Han Qiu,
• Abstract summary: Motivated by social psychology principles, we propose a novel strategy named textscPeT that inspires LLMs to integrate diverse human perspectives and self-regulate their responses. Rigorous evaluations and ablation studies are conducted on two commercial LLMs and three open-source LLMs, revealing textscPeT's superiority in producing less harmful responses.
• Score: 16.85625861663094
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The common toxicity and societal bias in contents generated by large language models (LLMs) necessitate strategies to reduce harm. Present solutions often demand white-box access to the model or substantial training, which is impractical for cutting-edge commercial LLMs. Moreover, prevailing prompting methods depend on external tool feedback and fail to simultaneously lessen toxicity and bias. Motivated by social psychology principles, we propose a novel strategy named \textbf{perspective-taking prompting (\textsc{PeT})} that inspires LLMs to integrate diverse human perspectives and self-regulate their responses. This self-correction mechanism can significantly diminish toxicity (up to $89\%$) and bias (up to $73\%$) in LLMs' responses. Rigorous evaluations and ablation studies are conducted on two commercial LLMs (ChatGPT and GLM) and three open-source LLMs, revealing \textsc{PeT}'s superiority in producing less harmful responses, outperforming five strong baselines.

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