Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias
<|reference_start|>Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias: 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.<|reference_end|>
@article{xu2024walking,
    title={Walking in Others' Shoes: How Perspective-Taking Guides Large Language
  Models in Reducing Toxicity and Bias},
    author={Rongwu Xu, Zi'an Zhou, Tianwei Zhang, Zehan Qi, Su Yao, Ke Xu, Wei Xu,
  Han Qiu},
    journal={arXiv preprint arXiv:2407.15366},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15366},
    primaryClass={cs.CL cs.AI cs.CY}
}