Large Language Models (LLMs) have recently transformed both the academic and industrial
landscapes due to their remarkable capacity to understand, analyze, and generate texts …

Knowledge editing has been increasingly adopted to correct the false or outdated
knowledge in Large Language Models (LLMs). Meanwhile, one critical but under-explored …

Multi-hop Question Answering (MQA) under knowledge editing (KE) is a key challenge in
Large Language Models (LLMs). While best-performing solutions in this domain use a plan …

Large Language Models (LLMs) have shown proficiency in question-answering tasks but
often struggle to integrate real-time knowledge, leading to potentially outdated or inaccurate …

Large Language Models (LLMs) suffer from hallucinations, referring to the non-factual
information in generated content, despite their superior capacities across tasks. Meanwhile …

Model editing has become an increasingly popular alternative for efficiently updating
knowledge within language models. Current methods mainly focus on reliability …

Although Large Language Models (LLMs) have shown strong performance in Multi-hop
Question Answering (MHQA) tasks, their real reasoning ability remains exploration. Current …

Multi-hop question answering (MQA) under knowledge editing (KE) has garnered significant
attention in the era of large language models. However, existing models for MQA under KE …

Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and
find multiple relevant supporting facts. However, Existing large language models'(LLMs) …

Abstract Model editing has become a promising method for precisely and effectively
updating knowledge in language models. In this paper, we investigate knowledge …