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A Comprehensive Study of Knowledge Editing for Large Language Models
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Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising from their extensive parameterization. This challenge is further intensified by the dynamic nature of the world, necessitating frequent updates to LLMs to correct outdated information or integrate new knowledge, thereby ensuring their continued relevance. Note that many applications demand continual model adjustments post-training to address deficiencies or undesirable behaviors. There is an increasing interest in efficient, lightweight methods for on-the-fly model modifications. To this end, recent years have seen a burgeoning in the techniques of knowledge editing for LLMs, which aim to efficiently modify LLMs' behaviors within specific domains while preserving overall performance across various inputs. In this paper, we first define the knowledge editing problem and then provide a comprehensive review of cutting-edge approaches. Drawing inspiration from educational and cognitive research theories, we propose a unified categorization criterion that classifies knowledge editing methods into three groups: resorting to external knowledge, merging knowledge into the model, and editing intrinsic knowledge. Furthermore, we introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches. Additionally, we provide an in-depth analysis of knowledge location, which can give a deeper understanding of the knowledge structures inherent within LLMs. Finally, we discuss several potential applications of knowledge editing, outlining its broad and impactful implications.
Forward citations
Cited by 18 Pith papers
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A mixture-of-experts LLM trained with reinforcement learning to perform retrieval from its own parametric memory can replace external retrieval in some settings, at lower latency.
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Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs
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Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing
MOIR: estimating the preservation covariance from a model's own random-token generations reduces collapse of math/code capabilities in some knowledge-editing settings, but the claimed consistency is not supported by t...
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Soft context compression via explicit depth/width information transmission over frozen LLM hidden states beats prior gist-token compressors on six QA benchmarks.
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Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning
An MLLM unlearning method and benchmark that aim to erase targeted private facts while preserving image understanding.
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Towards Meta-Cognitive Knowledge Editing for Multimodal LLMs
CogEdit and MIND shift multimodal knowledge editing toward evaluating and enabling meta-cognitive skills: self-awareness, boundary monitoring, and noise robustness.
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Towards a Principled Evaluation of Knowledge Editors
The choice of evaluation metric, generation length, and edit batch size changes the ranking of knowledge editors, and exact string matching produces false positives.
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An interactive chain-of-thought editor with prune and graft interventions reports large accuracy gains, but without error bars, code, or a comparison to existing editing tools.
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ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
ChainEdit uses knowledge-graph logical rules, filtered by an LLM, to propagate a single fact edit to dependent facts and lifts logical generalization on RIPPLE EDITS by about 40 points.
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The Compositional Architecture of Regret in Large Language Models
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Model Merging for Knowledge Editing
R-SFT plus task-vector scaling and pruning is proposed for knowledge editing, but the claimed sequential-editing advantage is not validated by the reported experiments.
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