3 papers
cs.CL2026
Can LLMs Time Travel? Enhancing Temporal Consistency in Legal Agentic Search through Reinforcement Learning
Wei Fan, Yining Zhou, Mufan Zhang +8
While large language models (LLMs) augmented with agentic search capabilities show promise for legal reasoning, they overlook a fundamental constraint that applicable law must matc…
cs.CL2025
LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation
Haitao Li, Yifan Chen, Yiran Hu +7
Retrieval-augmented generation (RAG) has proven highly effective in improving large language models (LLMs) across various domains. However, there is no benchmark specifically desig…
cs.CL2024
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
Weihang Su, Changyue Wang, Qingyao Ai +4
Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectivene…