6 papers
Can LLM Annotations Replace User Clicks for Learning to Rank?
Lulu Yu, Keping Bi, Jiafeng Guo +4
Large-scale supervised data is essential for training modern ranking models, but obtaining high-quality human annotations is costly. Click data has been widely used as a low-cost a…
VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
Haosheng Qian, Yixing Fan, Jiafeng Guo +4
Retrieval-Augmented Generation (RAG) has emerged as a crucial approach for enhancing the responses of large language models (LLMs) with external knowledge sources. Despite the impr…
Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation
Hengran Zhang, Keping Bi, Jiafeng Guo +4
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating retrieved information. Standard retrieval process prioritized relevance, focusing on top…
Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation
Hengran Zhang, Minghao Tang, Keping Bi +5
This paper explores the use of large language models (LLMs) for annotating document utility in training retrieval and retrieval-augmented generation (RAG) systems, aiming to reduce…
Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
Hengran Zhang, Keping Bi, Jiafeng Guo +5
Dense retrieval is a crucial task in Information Retrieval (IR), serving as the basis for downstream tasks such as re-ranking and augmenting generation. Recently, large language mo…
Unbiased Learning to Rank with Query-Level Click Propensity Estimation: Beyond Pointwise Observation and Relevance
Lulu Yu, Keping Bi, Jiafeng Guo +3
Most existing unbiased learning-to-rank (ULTR) approaches are based on the user examination hypothesis, which assumes that users will click a result only if it is both relevant and…