Trustworthy Information Retrieval in the LLM Era: Bias, Unfairness, and Hallucination

作者：Sunhao Dai, Chen Xu, Shicheng Xu, ZhongXiang Sun, Liang Pang, Zhenhua Dong, Jun Xu · 发表于：SIGIR-AP · 年份：2025 · DOI：10.1145/3767695.3769670 · 被引用次数：4 · 研究领域：Computer Science

The rapid progress of large language models (LLMs) has fundamentally reshaped information retrieval (IR) systems, including search engines and recommender systems, by enabling new capabilities and interaction paradigms. However, the integration of LLMs into IR pipelines also brings pressing challenges to trustworthiness, particularly in the form of bias, unfairness, and hallucination, which can significantly disrupt the information ecosystem. This tutorial provides a comprehensive overview of these challenges and their emerging mitigation strategies. We begin by presenting a unified perspective that frames bias, unfairness, and hallucination as manifestations of distribution mismatch, with mitigation strategies broadly conceptualized under distribution alignment. Building on this framework, we examine how these issues arise across three critical stages of LLM-integrated IR systems: data collection, model development, and result evaluation. For each stage, we systematically review recent findings, characterize distinct types of bias, unfairness, and hallucination, and discuss corresponding mitigation approaches. Finally, we outline open problems and highlight promising research directions that can advance trustworthy IR in the LLM era. By bridging multiple strands of research, this tutorial aims to raise awareness and provide actionable insights for researchers, practitioners, and stakeholders in both the IR and broader AI communities.