53 citations · 258 across the 27 of their papers we have counts for
5 papers · 1 filter
GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou
Ninglu Shao, Jinshan Wang, Chenxu Wang +3
Currently, short video platforms have become the primary place for individuals to share experiences and obtain information. To better meet users' needs for acquiring information wh…
Unified Generative Search and Recommendation
Teng Shi, Jun Xu, Xiao Zhang +4
Modern commercial platforms typically offer both search and recommendation functionalities to serve diverse user needs, making joint modeling of these tasks an appealing direction.…
Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
Teng Shi, Jun Xu, Xiao Zhang +4
Recently, the personalization of Large Language Models (LLMs) to generate content that aligns with individual user preferences has garnered widespread attention. Personalized Retri…
QE-RAG: A Robust Retrieval-Augmented Generation Benchmark for Query Entry Errors
Kepu Zhang, Zhongxiang Sun, Weijie Yu +5
Retriever-augmented generation (RAG) has become a widely adopted approach for enhancing the factual accuracy of large language models (LLMs). While current benchmarks evaluate the…
ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding
Zhongxiang Sun, Qipeng Wang, Weijie Yu +6
Retrieval-Augmented Generation (RAG) systems for Large Language Models (LLMs) hold promise in knowledge-intensive tasks but face limitations in complex multi-step reasoning. While…