用强化学习自动优化招聘模型，减少语言偏见。
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring
- 通过强化学习直接利用任务表现提升反馈进行微调。
- 在真实招聘平台和公开数据集上显著提升职位描述的多样性。
- 适合关注算法公平性与大模型落地应用的研究者。
基础模型需微调以确保生成内容符合特定任务目标。自动化微调困难，因通常需昂贵的人工反馈。本文提出AutoRefine方法，利用强化学习实现针对性微调，直接使用下游任务性能提升作为反馈信号。以算法招聘平台中的语言偏见问题为例，生成模型旨在重写职位描述，使推荐系统匹配更多元的候选人。模型能检测并调节职位描述中的偏见，满足多样性和公平性要求。在公开招聘数据集和真实招聘平台上的实验表明，大语言模型可有效识别并缓解现实世界中的偏见问题。
原文摘要 · Abstract (English)
Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it typically needs human feedback that can be expensive to acquire. We present AutoRefine, a method that leverages reinforcement learning for targeted fine-tuning, utilizing direct feedback from measurable performance improvements in specific downstream tasks. We demonstrate the method for a problem arising in algorithmic hiring platforms where linguistic biases influence a recommendation system. In this setting, a generative model seeks to rewrite given job specifications to receive more diverse candidate matches from a recommendation engine which matches jobs to candidates. Our model detects and regulates biases in job descriptions to meet diversity and fairness criteria. The experiments on a public hiring dataset and a real-world hiring platform showcase how large language models can assist in identifying and mitigation biases in the real world.
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