146 citations · 408 across the 42 of their papers we have counts for
46 papers
LexReward: A Taxonomy-Driven Reward Framework for Legal Language Models
Yida Cai, Xin Dai, Bingxiang He +5
Legal language models require reward signals that capture not only answer correctness but also the multidimensional quality of legal responses. Existing reward methods, however, of…
LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases
Yida Cai, Ranjuexiao Hu, Huiyuan Xie +6
Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases remain underexplored in the field…
LexChain: Modeling Legal Reasoning Chains for Chinese Tort Case Analysis
Huiyuan Xie, Chenyang Li, Huining Zhu +4
Legal reasoning is a fundamental component of legal analysis and decision-making. Existing computational approaches to legal reasoning predominantly rely on generic reasoning frame…
VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation
Yubo Sun, Chunyi Peng, Yukun Yan +5
Visual Retrieval-Augmented Generation (VRAG) has emerged as a promising paradigm for equipping Vision-Language Models (VLMs) with external visual evidence, enabling them to go beyo…
KARE-RAG: Knowledge-Aware Refinement and Enhancement for RAG
Yongjian Li, HaoCheng Chu, Yukun Yan +7
Retrieval-Augmented Generation (RAG) equips large language models with external knowledge and is central to knowledge-intensive tasks. As RAG systems enter real-world use, generato…
A*-Thought: Efficient Reasoning via Bidirectional Compression for Low-Resource Settings
Xiaoang Xu, Shuo Wang, Xu Han +6
Large Reasoning Models (LRMs) achieve superior performance by extending the thought length. However, a lengthy thinking trajectory leads to reduced efficiency. Most of the existing…
ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generation
Hao Chen, Yukun Yan, Sen Mei +9
Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge to improve factuality. However, existing RAG systems frequently underutilize the…
UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation
Yuxuan Chen, Dewen Guo, Sen Mei +12
Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate res…
KBAlign: Efficient Self Adaptation on Specific Knowledge Bases
Zheni Zeng, Yuxuan Chen, Shi Yu +7
Although retrieval-augmented generation (RAG) remains essential for knowledge-based question answering (KBQA), current paradigms face critical challenges under specific domains. Ex…
Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs
Cheng Gao, Chaojun Xiao, Zhenghao Liu +3
Legal case retrieval (LCR) aims to provide similar cases as references for a given fact description. This task is crucial for promoting consistent judgments in similar cases, effec…
RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards
Xinze Li, Sen Mei, Zhenghao Liu +9
Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To a…
VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
Shi Yu, Chaoyue Tang, Bokai Xu +8
Retrieval-augmented generation (RAG) is an effective technique that enables large language models (LLMs) to utilize external knowledge sources for generation. However, current RAG…
DeepNote: Note-Centric Deep Retrieval-Augmented Generation
Ruobing Wang, Qingfei Zhao, Yukun Yan +9
Retrieval-Augmented Generation (RAG) mitigates factual errors and hallucinations in Large Language Models (LLMs) for question-answering (QA) by incorporating external knowledge. Ho…
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework
Kunlun Zhu, Yifan Luo, Dingling Xu +10
Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RA…
PersLLM: A Personified Training Approach for Large Language Models
Zheni Zeng, Jiayi Chen, Huimin Chen +5
Large language models (LLMs) exhibit human-like intelligence, enabling them to simulate human behavior and support various applications that require both humanized communication an…
Advancing LLM Reasoning Generalists with Preference Trees
Lifan Yuan, Ganqu Cui, Hanbin Wang +12
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Finetuned from Mistral-7B and CodeLlama-70B, Eurus models achieve state-of-the-art results amon…
MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization
Zhiyu Yang, Zihan Zhou, Shuo Wang +10
Scientific data visualization plays a crucial role in research by enabling the direct display of complex information and assisting researchers in identifying implicit patterns. Des…
From Text to CQL: Bridging Natural Language and Corpus Search Engine
Luming Lu, Jiyuan An, Yujie Wang +8
Natural Language Processing (NLP) technologies have revolutionized the way we interact with information systems, with a significant focus on converting natural language queries int…
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset
Haoyu Wang, Shuo Wang, Yukun Yan +9
Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only l…
Cleaner Pretraining Corpus Curation with Neural Web Scraping
Zhipeng Xu, Zhenghao Liu, Yukun Yan +3
The web contains large-scale, diverse, and abundant information to satisfy the information-seeking needs of humans. Through meticulous data collection, preprocessing, and curation,…
ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling
Zhipeng Xu, Zhenghao Liu, Yukun Yan +7
Large Language Models (LLMs) have demonstrated strong performance across a wide range of NLP tasks. However, they often exhibit suboptimal behaviors and inconsistencies when expose…
OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models
Yang Liu, Meng Xu, Shuo Wang +7
Modern large language models (LLMs) should generally benefit individuals from various cultural backgrounds around the world. However, most recent advanced generative evaluation ben…
INTERVENOR: Prompting the Coding Ability of Large Language Models with the Interactive Chain of Repair
Hanbin Wang, Zhenghao Liu, Shuo Wang +4
This paper introduces INTERVENOR (INTERactiVE chaiN Of Repair), a system designed to emulate the interactive code repair processes observed in humans, encompassing both code diagno…
Modeling User Viewing Flow Using Large Language Models for Article Recommendation
Zhenghao Liu, Zulong Chen, Moufeng Zhang +6
This paper proposes the User Viewing Flow Modeling (SINGLE) method for the article recommendation task, which models the user constant preference and instant interest from user-cli…