Taolin Zhang

Despite substantial advances in large language models (LLMs), producing factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucination and the limitations of LLMs in bridging long-tail knowledge gaps. To address...

Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To enhance the overall efficiency, existing methods often rely on aggressive graph topology evolution for agents (e.g., node or edge pruning), which risks prematurely...

Recently, large language models (LLMs) have demonstrated impressive performance but still suffer from hallucinations. Model editing has been proposed as a means to correct factual inaccuracies. A challenging scenario is sequential model editing (SME), which aims to rectify errors continuously, ra...