Abstract
Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining. Lifelong model editing is the most challenging task that caters to the continuous editing requirements of LLMs. Prior works primarily focus on single or batch editing; nevertheless, these methods fall short in lifelong editing scenarios due to catastrophic knowledge forgetting and the degradation of model performance. Although retrieval-based methods alleviate these issues, they are impeded by slow and cumbersome processes of integrating the retrieved knowledge into the model. In this work, we introduce RECIPE, a RetriEval-augmented ContInuous Prompt lEarning method, to boost editing efficacy and inference efficiency in lifelong learning. RECIPE first converts knowledge statements into short and informative continuous prompts, prefixed to the LLM's input query embedding, to efficiently refine the response grounded on the knowledge. It further integrates the Knowledge Sentinel (KS) that acts as an intermediary to calculate a dynamic threshold, determining whether the retrieval repository contains relevant knowledge. Our retriever and prompt encoder are jointly trained to achieve editing properties, i.e., reliability, generality, and locality. In our experiments, RECIPE is assessed extensively across multiple LLMs and editing datasets, where it achieves superior editing performance. RECIPE also demonstrates its capability to maintain the overall performance of LLMs alongside showcasing fast editing and inference speed.
Abstract (translated)
模型编辑旨在在不需要昂贵重新训练的情况下，纠正大型语言模型（LLMs）中的过时或错误知识。终身模型编辑是满足LLMs连续编辑需求的最具有挑战性的任务。先前的研究主要集中在单次或批量编辑；然而，由于知识遗忘和模型性能的退化，这些方法在终身编辑场景中效果不佳。尽管基于检索的方法可以缓解这些问题，但它们受到将检索到的知识 integration到模型过程中缓慢和繁琐的过程所阻碍。在本文中，我们引入了RECIPE，一种增强型连续提示学习方法，以提高终身学习中的编辑效果和推理效率。 RECIPE首先将知识陈述转换为短而信息丰富的连续提示，附着在LLM的输入查询嵌入之前，以有效地基于知识进行回答的优化。它还引入了知识守护者（KS），作为中间人计算动态阈值，以确定检索存储库中是否包含相关知识。我们的检索器和提示编码器是联合训练的，以实现编辑特性，即可靠性、普适性和局部性。 在我们的实验中，RECIPE在多个LLM和编辑数据集上进行了广泛评估，在这些数据集上取得了卓越的编辑性能。RECIPE还展示了其在展示快速编辑和推理速度的同时，保持LLM整体性能的能力。
URL
https://arxiv.org/abs/2405.03279
