7 citations · 15 across the 16 of their papers we have counts for
4 papers · 1 filter
TeamCMU at Touché: Adversarial Co-Evolution for Advertisement Integration and Detection in Conversational Search
To Eun Kim, João Coelho, Gbemileke Onilude +1
As conversational search engines increasingly adopt generation-based paradigms powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), the integration of…
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:…
LTRR: Learning To Rank Retrievers for LLMs
To Eun Kim, Fernando Diaz
Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types…
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…