推理时实时过滤偏见，无需重训就能让大模型更公平
BiasFilter: An Inference-Time Debiasing Framework for Large Language Models
- 通过实时评估生成文本，动态筛选不公平内容
- 在多个大模型上显著降低社会偏见，同时保持生成质量
- 适合希望快速部署公平性改进的开发者和应用方
缓解大型语言模型中的社会偏见已成为重要研究方向。然而，现有方法通常成本高昂、效果有限，难以扩展至更大模型或开放式生成任务。为此，本文提出BiasFilter——一种与模型无关、可在推理阶段使用的去偏框架，可无缝集成于开源及API类大模型。该方法不依赖重新训练或参数修改，而是通过实时过滤生成输出实现公平性约束：每隔若干个词评估一次中间结果，维护候选延续集合，并基于公平性奖励信号逐步剔除低得分片段。为此，我们构建了一个公平性偏好数据集，并训练了一个隐式奖励模型以评估生成响应中的词级公平性。大量实验表明，BiasFilter能有效降低多种大模型的社会偏见，同时保持整体生成质量。
原文摘要 · Abstract (English)
Mitigating social bias in large language models (LLMs) has become an increasingly important research objective. However, existing debiasing methods often incur high human and computational costs, exhibit limited effectiveness, and struggle to scale to larger models and open-ended generation tasks. To address these limitations, this paper proposes BiasFilter, a model-agnostic, inference-time debiasing framework that integrates seamlessly with both open-source and API-based LLMs. Instead of relying on retraining with balanced data or modifying model parameters, BiasFilter enforces fairness by filtering generation outputs in real time. Specifically, it periodically evaluates intermediate outputs every few tokens, maintains an active set of candidate continuations, and incrementally completes generation by discarding low-reward segments based on a fairness reward signal. To support this process, we construct a fairness preference dataset and train an implicit reward model to assess token-level fairness in generated responses. Extensive experiments demonstrate that BiasFilter effectively mitigates social bias across a range of LLMs while preserving overall generation quality.
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