2 citations · 3 across the 7 of their papers we have counts for
7 papers
RRC: Unlocking Generative Reward Models in LLM Reinforcement Learning via Ranking-Based Reward Construction
Chenglong Wang, Ziming Zhu, Yifu Huo +9
Recent advances in reward modeling show a paradigm shift from discriminative reward models to generative reward models. However, despite their strong capabilities in response ranki…
SERM: Self-Evolving Relevance Model with Agent-Driven Learning from Massive Query Streams
Chenglong Wang, Canjia Li, Xingzhao Zhu +9
Due to the dynamically evolving nature of real-world query streams, relevance models struggle to generalize to practical search scenarios. A sophisticated solution is self-evolutio…
Event-enhanced Retrieval in Real-time Search
Yanan Zhang, Xiaoling Bai, Tianhua Zhou
The embedding-based retrieval (EBR) approach is widely used in mainstream search engine retrieval systems and is crucial in recent retrieval-augmented methods for eliminating LLM i…
WebCiteS: Attributed Query-Focused Summarization on Chinese Web Search Results with Citations
Haolin Deng, Chang Wang, Xin Li +6
Enhancing the attribution in large language models (LLMs) is a crucial task. One feasible approach is to enable LLMs to cite external sources that support their generations. Howeve…
Event-driven Real-time Retrieval in Web Search
Nan Yang, Shusen Zhang, Yannan Zhang +4
Information retrieval in real-time search presents unique challenges distinct from those encountered in classical web search. These challenges are particularly pronounced due to th…
Event-Centric Query Expansion in Web Search
Yanan Zhang, Weijie Cui, Yangfan Zhang +5
In search engines, query expansion (QE) is a crucial technique to improve search experience. Previous studies often rely on long-term search log mining, which leads to slow updates…