6 citations · 8 across the 3 of their papers we have counts for
5 papers
Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking
Songhao Wu, Quan Tu, Mingjie Zhong +4
In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedb…
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search
Songhao Wu, Quan Tu, Hong Liu +6
Session search involves a series of interactive queries and actions to fulfill user's complex information need. Current strategies typically prioritize sequential modeling for deep…
Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search
Yedan Shen, Kaixin Wu, Yuechen Ding +6
Generative retrieval (GR) has revolutionized document retrieval with the advent of large language models (LLMs), and LLM-based GR is gradually being adopted by the industry. Despit…
Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning
Hong Liu, Saisai Gong, Yixin Ji +3
With the rapid advancement of pre-trained large language models (LLMs), recent endeavors have leveraged the capabilities of LLMs in relevance modeling, resulting in enhanced perfor…
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search
Kaixin Wu, Yixin Ji, Zeyuan Chen +9
Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements o…