9 papers
AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering
Taolin Zhang, Dongyang Li, Chen Chen +5
Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These diffic…
Taming "Zombie'' Agents: A Markov State-Aware Framework for Resilient Multi-Agent Evolution
Taolin Zhang, Pukun Zhao, Qizhou Chen +5
Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To improve overall efficiency, existing methods o…
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…
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…
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…