98 citations · 486 across the 56 of their papers we have counts for
8 papers · 1 filter
From Novice to Expert: LLM Agent Policy Optimization via Step-wise Reinforcement Learning
Zhirui Deng, Zhicheng Dou, Yutao Zhu +4
The outstanding capabilities of large language models (LLMs) render them a crucial component in various autonomous agent systems. While traditional methods depend on the inherent k…
OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain
Shuting Wang, Jiejun Tan, Zhicheng Dou +1
As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical do…
HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
Jiejun Tan, Zhicheng Dou, Wen Wang +3
Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowled…
Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
Guanting Dong, Xiaoshuai Song, Yutao Zhu +3
Following natural instructions is crucial for the effective application of Retrieval-Augmented Generation (RAG) systems. Despite recent advancements in Large Language Models (LLMs)…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
Guanting Dong, Yutao Zhu, Chenghao Zhang +3
Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of large language models (LLMs). However, the difficulty of aligning the…
One Token Can Help! Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models
Yutao Zhu, Zhaoheng Huang, Zhicheng Dou +1
Retrieval-augmented generation (RAG) is a promising way to improve large language models (LLMs) for generating more factual, accurate, and up-to-date content. Existing methods eith…
UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models
Zhaoheng Huang, Zhicheng Dou, Yutao Zhu +1
Large language models (LLMs) may generate text that lacks consistency with human knowledge, leading to factual inaccuracies or \textit{hallucination}. Existing research for evaluat…