用多智能体优化信息源选择，显著降低大模型检索偏见。
Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval
- 设计多智能体系统，动态筛选低偏见信息源。
- 实验显示偏见降低81.82%，优于传统检索方法。
- 适合关注AI公平性与可信知识获取的研究者。
大型语言模型（LLMs）通过生成式AI能力开启了生成应用的新时代。在此基础上，代理式AI代表了向自主、目标驱动系统的重要转变，能够推理、检索并执行任务。然而，这类系统也继承了内部与外部信息源中的偏见，严重影响检索结果的公平性与平衡性，进而削弱用户信任。为应对这一关键挑战，本文提出一种新型偏见缓解代理（Bias Mitigation Agent），该多智能体系统通过专业化代理协同工作，优化信息源的选择流程，确保检索内容既高度相关又最大限度减少偏见，促进公正均衡的知识传播。实验结果表明，相比基线的简单检索策略，偏见降低了81.82%。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have transformed the field of artificial intelligence by unlocking the era of generative applications. Built on top of generative AI capabilities, Agentic AI represents a major shift toward autonomous, goal-driven systems that can reason, retrieve, and act. However, they also inherit the bias present in both internal and external information sources. This significantly affects the fairness and balance of retrieved information, and hence reduces user trust. To address this critical challenge, we introduce a novel Bias Mitigation Agent, a multi-agent system designed to orchestrate the workflow of bias mitigation through specialized agents that optimize the selection of sources to ensure that the retrieved content is both highly relevant and minimally biased to promote fair and balanced knowledge dissemination. The experimental results demonstrate an 81.82\% reduction in bias compared to a baseline naive retrieval strategy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。