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Multilingual and Domain-Agnostic Tip-of-the-Tongue Query Generation for Simulated Evaluation
Xuhong He, To Eun Kim, Maik Fröbe +3
Tip-of-the-Tongue (ToT) retrieval benchmarks have largely focused on English, limiting their applicability to multilingual information access. In this work, we construct multilingu…
Evaluation of Agents under Simulated AI Marketplace Dynamics
To Eun Kim, Alireza Salemi, Hamed Zamani +1
Modern information access ecosystems consist of mixtures of systems, such as retrieval systems and large language models, and increasingly rely on marketplaces to mediate access to…
Overview of the TREC 2025 Tip-of-the-Tongue track
Jaime Arguello, Fernando Diaz, Maik Fröebe +2
Tip-of-the-tongue (ToT) known-item retrieval involves re-finding an item for which the searcher does not reliably recall an identifier. ToT information requests (or queries) are ve…
Tip of the Tongue Query Elicitation for Simulated Evaluation
Yifan He, To Eun Kim, Fernando Diaz +2
Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effective…
Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation
To Eun Kim, Fernando Diaz
Despite the central role of retrieval in retrieval-augmented generation (RAG) systems, much of the existing research on RAG overlooks the well-established field of fair ranking and…
MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Jushaan Singh Kalra, Xinran Zhao, To Eun Kim +3
Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals:…