用提示词让大模型推荐更公平，无需改模型就能降偏见。
Can Fairness Be Prompted? Prompt-Based Debiasing Strategies in High-Stakes Recommendations
- 设计三种提示词策略，让模型主动追求推荐公平性。
- 实验显示公平性最高提升74%，效果基本不下降。
- 适合想快速改善推荐系统偏见的非技术用户。
大型语言模型（LLMs）能通过姓名、代词等间接线索推断性别、年龄等敏感属性，导致推荐结果出现偏见。现有去偏方法需访问模型权重，计算成本高，普通用户无法使用。为此，我们研究了LLM推荐系统中的隐式偏见，并探索提示词是否可作为轻量级、易用的去偏手段。提出三种面向群体公平性的提示词去偏策略。据我们所知，这是首个聚焦于用户群体公平性的提示词去偏研究。在3个LLM、4种提示模板、9种敏感属性值和2个数据集上的实验表明，通过指令模型保持公平，可使公平性提升最高达74%，同时保持相近的推荐效果；但在某些情况下可能过度推荐特定人口群体。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can infer sensitive attributes such as gender or age from indirect cues like names and pronouns, potentially biasing recommendations. While several debiasing methods exist, they require access to the LLMs' weights, are computationally costly, and cannot be used by lay users. To address this gap, we investigate implicit biases in LLM Recommenders (LLMRecs) and explore whether prompt-based strategies can serve as a lightweight and easy-to-use debiasing approach. We contribute three bias-aware prompting strategies for LLMRecs. To our knowledge, this is the first study on prompt-based debiasing approaches in LLMRecs that focuses on group fairness for users. Our experiments with 3 LLMs, 4 prompt templates, 9 sensitive attribute values, and 2 datasets show that our proposed debiasing approach, which instructs an LLM to be fair, can improve fairness by up to 74% while retaining comparable effectiveness, but might overpromote specific demographic groups in some cases.
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