用提示工程提升大模型在表格数据上的群体公平性
Improving LLM Group Fairness on Tabular Data via In-Context Learning
- 通过优化提示、选择示例等方式引导模型生成更公平的预测
- 在四个数据集上实现更高群体公平性，同时保持高准确率
- 适合关注算法公平性的数据科学从业者参考
大语言模型在低数据场景下对表格预测任务表现出色，依赖其内部知识和指令学习能力。然而，它们可能无法生成满足群体公平性的结果，即在不同群体间产生均衡的预测表现。值得注意的是，自然语言任务中的传统去偏方法难以直接适用于表格场景。本文系统研究了四种提升大模型在表格数据上群体公平性的方法：公平提示优化、软提示调优、少样本示例的策略性选择，以及基于思维链推理的自修正预测。在四个表格数据集上，使用开源与专有大模型进行实验，结果表明这些方法能有效提升人口均等性（demographic parity），同时维持高水平的整体性能。分析为实践者根据具体需求和约束选择合适方法提供了可操作的指导。
原文摘要 · Abstract (English)
Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instructions and examples. However, LLMs can fail to generate predictions that satisfy group fairness, that is, produce equitable outcomes across groups. Critically, conventional debiasing approaches for natural language tasks do not directly translate to mitigating group unfairness in tabular settings. In this work, we systematically investigate four empirical approaches to improve group fairness of LLM predictions on tabular datasets, including fair prompt optimization, soft prompt tuning, strategic selection of few-shot examples, and self-refining predictions via chain-of-thought reasoning. Through experiments on four tabular datasets using both open-source and proprietary LLMs, we show the effectiveness of these methods in enhancing demographic parity while maintaining high overall performance. Our analysis provides actionable insights for practitioners in selecting the most suitable approach based on their specific requirements and constraints.
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