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memory-based model

A memory-based model is an artificial intelligence framework designed to update or correct the behavior and knowledge of a pre-trained model by storing edits in an explicit external memory rather than modifying the base model parameters. When a new input is received, the framework uses a retrieval mechanism or scope classifier to determine whether the query relates to any stored updates. If a match is found, the relevant edit information is fetched to modulate or override the base model output, often using an auxiliary counterfactual model or structured retrieval. By keeping the core weights of the original model unchanged, this approach enables flexible, continuous knowledge updates while preventing catastrophic forgetting and minimizing unintended interference with unrelated general capabilities.

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Editing Large Language Models: Problems, Methods, and Opportunities

Editing Large Language Models: Problems, Methods, and Opportunities

Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang

OrganizationsDonghai LaboratoryNational University of SingaporeNUS-NCS Joint LabZhejiang UniversityZhejiang University - Ant Group Joint Laboratory of Knowledge Graph

Why you should read this

Presents a unified taxonomy and empirical evaluation of large language model editing techniques across multiple architectures and settings, providing a standardized benchmark to assess reliability, generalization, locality, and computational efficiency.

Despite the ability to train capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To this end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to efficiently alter the behavior of LLMs within a specific domain without negatively impacting performance across other inputs. This paper embarks on a deep exploration of the problems, methods, and opportunities related to model editing for LLMs. In particular, we provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. We also build a new benchmark dataset to facilitate a more robust evaluation and pinpoint enduring issues intrinsic to existing techniques. Our objective is to provide valuable insights into the effectiveness and feasibility of each editing technique, thereby assisting the community in making informed decisions on the selection of the most appropriate method for a specific task or context¹.

Added

2026-09-29