Summary of Walking in Others’ Shoes: How Perspective-taking Guides Large Language Models in Reducing Toxicity and Bias, by Rongwu Xu et al.
Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias
by Rongwu Xu, Zi’an Zhou, Tianwei Zhang, Zehan Qi, Su Yao, Ke Xu, Wei Xu, Han Qiu
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel strategy called perspective-taking prompting (PeT) to reduce toxicity and bias in large language models (LLMs). The approach draws from social psychology principles and enables LLMs to integrate diverse human perspectives and self-regulate their responses. This mechanism can significantly diminish toxicity by up to 89% and bias by up to 73% in LLMs’ responses. The authors conduct rigorous evaluations and ablation studies on two commercial LLMs (ChatGPT and GLM) and three open-source LLMs, demonstrating PeT’s superiority in producing less harmful responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better by reducing the mean things they say. Right now, these models can be very biased and toxic, which is a big problem. The authors of this paper want to fix that. They created a new way to make the models think more like people do, by considering different perspectives. This helps the models be kinder and fairer in what they say. The authors tested their method on several language models and found it works really well – it can reduce mean things said by up to 89%! |
Keywords
* Artificial intelligence * Prompting
