EvoEdit: 通过潜在扰动增强和知识驱动参数融合实现终身自由文本知识编辑
EvoEdit: Lifelong Free-Text Knowledge Editing through Latent Perturbation Augmentation and Knowledge-driven Parameter Fusion
AI总结：
EvoEdit通过潜在扰动增强和知识驱动参数融合，实现终身自由文本知识编辑，有效解决知识更新与遗忘问题。
摘要：
在部署后调整大型语言模型（LLMs）的过时知识仍然是一个主要挑战。这一困难促使了知识编辑的发展，旨在准确且高效地修改模型的内部（参数）知识，而无需重新从头训练。然而，现有方法存在两个局限性。首先，它们依赖于结构化的三元组，这些三元组与LLM预训练的自由文本性质不匹配，并且无法捕捉事实之间的细微关系。其次，它们通常支持一次性的知识更新，对连续或终身编辑问题的研究相对有限。为了解决这些差距，我们提出一个新的任务，即终身自由文本知识编辑（LF-Edit），使模型能够整合以自然语言表达的更新，并支持随时间推移的持续编辑。尽管其前景广阔，LF-Edit面临双重挑战：在整合新知识的同时，减轻对先前信息的遗忘。为了促进对这一新任务的研究，我们构建了一个大规模基准，即多级终身自由文本编辑基准（MRLF-Bench），包含16,835个自由文本编辑请求。我们进一步设计了一个受认知启发的多级评估框架，涵盖四个层次：记忆、理解、受限理解和推理。为了应对LF-Edit固有的挑战，我们引入了一种名为EvoEdit的新方法，通过潜在扰动增强来增强知识注入，并通过知识驱动参数融合来保存先前信息。实验结果表明，EvoEdit在所提出的LF-Edit任务上显著优于现有知识编辑方法。
英文摘要：
Adjusting the outdated knowledge of large language models (LLMs) after deployment remains a major challenge. This difficulty has spurred the development of knowledge editing, which seeks to accurately and efficiently modify a model's internal (parametric) knowledge without retraining it from scratch. However, existing methods suffer from two limitations. First, they depend on structured triplets that are misaligned with the free-text nature of LLM pretraining and fail to capture the nuanced relationships among facts. Second, they typically support one-time knowledge updates, with relatively limited research on the problem of sequential or lifelong editing. To address these gaps, we propose a new task, Lifelong Free-text Knowledge Editing (LF-Edit), which enables models to incorporate updates expressed in natural language and supports continual editing over time. Despite its promise, LF-Edit faces the dual challenge of integrating new knowledge while mitigating the forgetting of prior information. To foster research on this new task, we construct a large-scale benchmark, Multi-Rank Lifelong Free-text Editing Benchmark (MRLF-Bench), containing 16,835 free-text edit requests. We further design a cognitively inspired multi-rank evaluation framework encompassing four levels: memorization, understanding, constrained comprehension, and reasoning. To tackle the challenges inherent in LF-Edit, we introduce a novel approach named EvoEdit that enhances knowledge injection through Latent Perturbation Augmentation and preserves prior information via Knowledge-driven Parameter Fusion. Experimental results demonstrate that EvoEdit substantially outperforms existing knowledge editing methods on the proposed LF-Edit task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
