4 citations · 7 across the 14 of their papers we have counts for
6 papers · 1 filter
Towards Unification of Hallucination Detection and Fact Verification for Large Language Models
Weihang Su, Jianming Long, Changyue Wang +5
Large Language Models (LLMs) frequently exhibit hallucinations, generating content that appears fluent and coherent but is factually incorrect. Such errors undermine trust and hind…
MemoryBench: A Benchmark for Memory and Continual Learning in LLM Systems
Qingyao Ai, Yichen Tang, Changyue Wang +3
Scaling up data, parameters, and test-time computation has been the mainstream methods to improve LLM systems (LLMsys), but their upper bounds are almost reached due to the gradual…
Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models
Changyue Wang, Weihang Su, Qingyao Ai +1
Large Reasoning Models (LRMs) extend large language models with explicit, multi-step reasoning traces to enhance transparency and performance on complex tasks. However, these reaso…
Decoupling Reasoning and Knowledge Injection for In-Context Knowledge Editing
Changyue Wang, Weihang Su, Qingyao Ai +2
Knowledge editing aims to efficiently update Large Language Models (LLMs) by modifying specific knowledge without retraining the entire model. Among knowledge editing approaches, i…
JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System
Weihang Su, Baoqing Yue, Qingyao Ai +6
This paper introduces JuDGE (Judgment Document Generation Evaluation), a novel benchmark for evaluating the performance of judgment document generation in the Chinese legal system.…
Parametric Retrieval Augmented Generation
Weihang Su, Yichen Tang, Qingyao Ai +6
Retrieval-augmented generation (RAG) techniques have emerged as a promising solution to enhance the reliability of large language models (LLMs) by addressing issues like hallucinat…