5 papers
Deep Research: A Systematic Survey
Zhengliang Shi, Yiqun Chen, Haitao Li +23
Large language models (LLMs) have rapidly evolved from text generators into powerful problem solvers. Yet, many open tasks demand critical thinking, multi-source, and verifiable ou…
Understanding Parametric Knowledge Injection in Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jingtong Wu +2
Context-grounded generation underpins many LLM applications, including long-document question answering (QA), conversational personalization, and retrieval-augmented generation (RA…
Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs
Zhikai Ding, Shiyu Ni, Keping Bi
Large vision-language models (LVLMs) demonstrate strong visual question answering (VQA) capabilities but are shown to hallucinate. A reliable model should perceive its knowledge bo…
Injecting External Knowledge into the Reasoning Process Enhances Retrieval-Augmented Generation
Minghao Tang, Shiyu Ni, Jiafeng Guo +1
Retrieval-augmented generation (RAG) has been widely adopted to augment large language models (LLMs) with external knowledge for knowledge-intensive tasks. However, its effectivene…
Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception
Shiyu Ni, Keping Bi, Jiafeng Guo +3
Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries, leading to confident yet incorre…