16 citations · 83 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
Are Long-LLMs A Necessity For Long-Context Tasks?
Hongjin Qian, Zheng Liu, Peitian Zhang +4
The learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we argue that the long-LLMs are not a necessity to solve long-contex…
Metacognitive Retrieval-Augmented Large Language Models
Yujia Zhou, Zheng Liu, Jiajie Jin +2
Retrieval-augmented generation have become central in natural language processing due to their efficacy in generating factual content. While traditional methods employ single-time…
Grounding Language Model with Chunking-Free In-Context Retrieval
Hongjin Qian, Zheng Liu, Kelong Mao +2
This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems ofte…
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…
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks
Xiaoxi Li, Zhicheng Dou, Yujia Zhou +1
Large language models (LLMs) have gained significant attention in various fields but prone to hallucination, especially in knowledge-intensive (KI) tasks. To address this, retrieva…