146 citations · 408 across the 42 of their papers we have counts for
7 papers · 1 filter
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…