诸如 Microsoft Copilot 和 Google Gemini 等对话式智能体可协助用户完成复杂的搜索任务，但常常生成误导性或虚构的参考文献。这削弱了用户信任，尤其是在医学和金融等高风险领域。可解释信息检索（XIR）旨在通过使搜索结果更加透明和可解释来解决这一问题。尽管大多数 XIR 研究是领域无关的，本文聚焦于审计——一个关键但尚未被充分探索的领域。我们认为 XIR 系统能够支持审计人员完成其复杂任务。我们概述了在该领域推进 XIR 的关键挑战和未来研究方向。
英文摘要：
Conversational agents such as Microsoft Copilot and Google Gemini assist users with complex search tasks but often generate misleading or fabricated references. This undermines trust, particularly in high-stakes domains such as medicine and finance. Explainable information retrieval (XIR) aims to address this by making search results more transparent and interpretable. While most XIR research is domain-agnostic, this paper focuses on auditing -- a critical yet underexplored area. We argue that XIR systems can support auditors in completing their complex task. We outline key challenges and future research directions to advance XIR in this domain.
Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying pers…
Product retrieval systems have served as the main entry for customers to discover and purchase products online. With increasing concerns on the transparency and accountability of AI systems, studies on explainable information retrieval has received more and more attention in the research community. Interestingly, in the domain of e-commerce, despite the extensive studies on explainable product recommendation, the studies of explainable product search is still in an early stage. In this paper, we study how to construct effective explainable product search by comparing model-agnostic explanation…
Retrieval models in information retrieval are used to rank documents for typically under-specified queries. Today machine learning is used to learn retrieval models from click logs and/or relevance judgments that maximizes an objective correlated with user satisfaction. As these models become increasingly powerful and sophisticated, they also become harder to understand. Consequently, it is hard for to identify artifacts in training, data specific biases and intents from a complex trained model like neural rankers even if trained purely on text features. EXS is a search system designed specifi…
Thanks to recent advancements in machine learning, vector-based methods have been adopted in many modern information retrieval (IR) systems. While showing promising retrieval performance, these approaches typically fail to explain why a particular document is retrieved as a query result to address explainable information retrieval(XIR). Knowledge graphs record structured information about entities and inherently explainable relationships. Most of existing XIR approaches focus exclusively on the retrieval model with little consideration on using existing knowledge graphs for providing an explan…
VERIRAG: A Post-Retrieval Auditing of Scientific Study Summaries
Cornell University（康奈尔大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of Illinois Urbana‑Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, Los Angeles（加州大学洛杉矶分校）
Product search is one of the most popular methods for customers to discover products online. Most existing studies on product search focus on developing effective retrieval models that rank items by their likelihood to be purchased. They, however, ignore the problem that there is a gap between how systems and customers perceive the relevance of items. Without explanations, users may not understand why product search engines retrieve certain items for them, which consequentially leads to imperfect user experience and suboptimal system performance in practice. In this work, we tackle this proble…
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong（香港中文大学）；Monash University（莫纳什大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
EndoFinder: Online Image Retrieval for Explainable Colorectal Polyp Diagnosis
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University（复旦大学基础医学院数字医学研究中心）；Shanghai Institute for Advanced Study of Zhejiang University（浙江大学上海高等研究院）；Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University（复旦大学附属中山医院内镜中心及内镜研究所）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；Alliance Manchester Business School, The University of Manchester（曼彻斯特大学联盟曼彻斯特商学院）
AgenticRAG: Tool-Augmented Foundation Models for Zero-Shot Explainable Recommender Systems
Department of Software \& Microelectronics Peking University Beijing, China；Department of Software \& Microelectronics Peking University Beijing, China hangli\；Department of Software \& Microelectronics Peking University Beijing, China zehua\；Department of Software \& Microelectronics Peking University Beijing, China xiaofan\；Economic Law School China University of Political Science；School of Computer Science Peking University Beijing, China
