16 citations · 16 across the 1 of their papers we have counts for
9 papers
Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
Yujia Zhou, Wenbo Zhang, Jingying Shao +10
Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although existing research mainly emphasizes accu…
From Matching to Generation: A Survey on Generative Information Retrieval
Xiaoxi Li, Jiajie Jin, Yujia Zhou +4
Information Retrieval (IR) systems are crucial tools for users to access information, which have long been dominated by traditional methods relying on similarity matching. With the…
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Xiaoxi Li, Guanting Dong, Jiajie Jin +5
Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. However, their extended r…
RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation
Xiaoxi Li, Jiajie Jin, Yujia Zhou +4
Large language models (LLMs) exhibit remarkable generative capabilities but often suffer from hallucinations. Retrieval-augmented generation (RAG) offers an effective solution by i…
AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant
Yujia Zhou, Zheng Liu, Zhicheng Dou
The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hal…
BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence
Jiajie Jin, Yutao Zhu, Yujia Zhou +1
Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update…