2 citations · 6 across the 14 of their papers we have counts for
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Can LLM Rerankers Predict Their Own Ranking Performance?
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available. Query performance prediction…
Understanding Parametric Knowledge Injection in Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jingtong Wu +2
Context-grounded generation underpins many LLM applications, including long-document question answering (QA), conversational personalization, and retrieval-augmented generation (RA…
Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jiafeng Guo +1
Retrieval-augmented generation (RAG) has been widely adopted to augment large language models (LLMs) with external knowledge for knowledge-intensive tasks. However, its effectivene…
Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank
Lulu Yu, Keping Bi, Shiyu Ni +1
Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods…
A Comparative Study of Training Objectives for Clarification Facet Generation
Shiyu Ni, Keping Bi, Jiafeng Guo +1
Due to the ambiguity and vagueness of a user query, it is essential to identify the query facets for the clarification of user intents. Existing work on query facet generation has…