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- Tsinghua UniversityCN36 papers
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29 papers · 1 filter
ICEGR: An Intent-Coherent End-to-End Generative Retrieval Framework for E-commerce Search
Jiayi Tuo, Hehan Li, Dongjun Fu +10
Generative Retrieval (GR) is promising for e-commerce search, yet existing methods struggle to maintain query-intent consistency throughout the training pipeline. First, semantic I…
Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration
Deqiang Huang, Jingbo Zhou, Xinjiang Lu +3
Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift and incomplete answers. We intr…
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…
Towards Next-Generation Recommender Systems: A Benchmark for Personalized Recommendation Assistant with LLMs
Jiani Huang, Shijie Wang, Liang-bo Ning +4
Recommender systems (RecSys) are widely used across various modern digital platforms and have garnered significant attention. Traditional recommender systems usually focus only on…
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…