22 citations · 74 across the 21 of their papers we have counts for
24 papers
FinSight: Towards Real-World Financial Deep Research
Jiajie Jin, Yuyao Zhang, Yimeng Xu +3
Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, w…
HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search
Jiajie Jin, Xiaoxi Li, Guanting Dong +5
Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG…
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning
Guanting Dong, Yifei Chen, Xiaoxi Li +7
Recently, large language models (LLMs) have shown remarkable reasoning capabilities via large-scale reinforcement learning (RL). However, leveraging the RL algorithm to empower eff…
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization
Yutao Zhu, Jiajie Jin, Hongjin Qian +3
Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizat…
Scent of Knowledge: Optimizing Search-Enhanced Reasoning with Information Foraging
Hongjin Qian, Zheng Liu
Augmenting large language models (LLMs) with external retrieval has become a standard method to address their inherent knowledge cutoff limitations. However, traditional retrieval-…
WebThinker: Empowering Large Reasoning Models with Deep Research Capability
Xiaoxi Li, Jiajie Jin, Guanting Dong +5
Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate impressive long-horizon reasoning capabilities. However, their reliance on static internal knowledge l…
Memory-enhanced Retrieval Augmentation for Long Video Understanding
Huaying Yuan, Zheng Liu, Minghao Qin +5
Efficient long-video understanding~(LVU) remains a challenging task in computer vision. Current long-context vision-language models~(LVLMs) suffer from information loss due to comp…
Does RAG Really Perform Bad For Long-Context Processing?
Kun Luo, Zheng Liu, Peitian Zhang +3
The efficient processing of long context poses a serious challenge for large language models (LLMs). Recently, retrieval-augmented generation (RAG) has emerged as a promising strat…
HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks
Hongjin Qian, Zheng Liu, Chao Gao +3
In real-world information-seeking scenarios, users have dynamic and diverse needs, requiring RAG systems to demonstrate adaptable resilience. To comprehensively evaluate the resili…
Boosting Long-Context Management via Query-Guided Activation Refilling
Hongjin Qian, Zheng Liu, Peitian Zhang +2
Processing long contexts poses a significant challenge for large language models (LLMs) due to their inherent context-window limitations and the computational burden of extensive k…
CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
Yiruo Cheng, Kelong Mao, Ziliang Zhao +6
Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attentio…
A Survey of Conversational Search
Fengran Mo, Kelong Mao, Ziliang Zhao +7
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) te…
Theoretical Insights into Fine-Tuning Attention Mechanism: Generalization and Optimization
Xinhao Yao, Hongjin Qian, Xiaolin Hu +5
Large Language Models (LLMs), built on Transformer architectures, exhibit remarkable generalization across a wide range of tasks. However, fine-tuning these models for specific tas…
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…
MemoRAG: Boosting Long Context Processing with Global Memory-Enhanced Retrieval Augmentation
Hongjin Qian, Zheng Liu, Peitian Zhang +4
Processing long contexts presents a significant challenge for large language models (LLMs). While recent advancements allow LLMs to handle much longer contexts than before (e.g., 3…
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…
Extending Llama-3's Context Ten-Fold Overnight
Peitian Zhang, Ninglu Shao, Zheng Liu +4
We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning. The entire training cycle is super efficient, which takes 8 hours on one 8xA800 (80G) GPU…
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…
Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection
Hongjin Qian, Zhicheng Dou, Jiejun Tan +6
Language models (LMs) have revolutionized the way we interact with information, but they often generate nonfactual text, raising concerns about their reliability. Previous methods…
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus
Hongjing Qian, Yutao Zhu, Zhicheng Dou +7
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web. In this task, called WebBrai…
Large Language Models Know Your Contextual Search Intent: A Prompting Framework for Conversational Search
Kelong Mao, Zhicheng Dou, Fengran Mo +3
Precisely understanding users' contextual search intent has been an important challenge for conversational search. As conversational search sessions are much more diverse and long-…
Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation
Hanxun Zhong, Zhicheng Dou, Yutao Zhu +2
Personalized dialogue systems explore the problem of generating responses that are consistent with the user's personality, which has raised much attention in recent years. Existing…
Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot
Hongjin Qian, Zhicheng Dou, Yutao Zhu +2
In this paper, we explore the problem of developing personalized chatbots. A personalized chatbot is designed as a digital chatting assistant for a user. The key characteristic of…
Pchatbot: A Large-Scale Dataset for Personalized Chatbot
Hongjin Qian, Xiaohe Li, Hanxun Zhong +6
Natural language dialogue systems raise great attention recently. As many dialogue models are data-driven, high-quality datasets are essential to these systems. In this paper, we i…