1 citations · 3 across the 19 of their papers we have counts for
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An Information-Theoretic Framework for Robust Large Language Model Editing
Qizhou Chen, Chengyu Wang, Taolin Zhang +1
Large Language Models (LLMs) have become indispensable tools in science, technology, and society, enabling transformative advances across diverse fields. However, errors or outdate…
MPrune: Hierarchical Communication Graph Pruning for Efficient Multi-Modal Multi-Agent Retrieval-Augmented Generation
Weizi Shao, Taolin Zhang, Zijie Zhou +3
Recent advancements in multi-modal retrieval-augmented generation (mRAG), which enhance multi-modal large language models (MLLMs) with external knowledge, have demonstrated that th…
QueueEDIT: Structural Self-Correction for Sequential Model Editing in LLMs
Taolin Zhang, Haidong Kang, Dongyang Li +3
Recently, large language models (LLMs) have demonstrated impressive results but still suffer from hallucinations. Model editing has been proposed to correct factual inaccuracies in…
BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering
Taolin Zhang, Dongyang Li, Qizhou Chen +2
Multi-hop question answering (QA) involves finding multiple relevant passages and performing step-by-step reasoning to answer complex questions. Previous works on multi-hop QA empl…
UniEdit: A Unified Knowledge Editing Benchmark for Large Language Models
Qizhou Chen, Dakan Wang, Taolin Zhang +4
Model editing aims to enhance the accuracy and reliability of large language models (LLMs) by efficiently adjusting their internal parameters. Currently, most LLM editing datasets…