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EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
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Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. To this end, many knowledge editing approaches for LLMs have emerged -- aiming to subtly inject/edit updated knowledge or adjust undesired behavior while minimizing the impact on unrelated inputs. Nevertheless, due to significant differences among various knowledge editing methods and the variations in task setups, there is no standard implementation framework available for the community, which hinders practitioners from applying knowledge editing to applications. To address these issues, we propose EasyEdit, an easy-to-use knowledge editing framework for LLMs. It supports various cutting-edge knowledge editing approaches and can be readily applied to many well-known LLMs such as T5, GPT-J, LlaMA, etc. Empirically, we report the knowledge editing results on LlaMA-2 with EasyEdit, demonstrating that knowledge editing surpasses traditional fine-tuning in terms of reliability and generalization. We have released the source code on GitHub, along with Google Colab tutorials and comprehensive documentation for beginners to get started. Besides, we present an online system for real-time knowledge editing, and a demo video.
Forward citations
Cited by 11 Pith papers
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FABLE: Fine-grained Fact Anchoring for Unstructured Model Editing
FABLE decouples fine-grained fact anchoring in shallow Transformer layers from deeper text generation to improve specific fact access while preserving holistic editing performance.
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Exposing the Illusion of Erasure in Knowledge Editing for LLMs
Knowledge editing methods redistribute and suppress rather than overwrite facts in LLMs, creating narrow vulnerable regions in representation space that adversarial prompts can exploit.
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LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
LOKI enables memory-free lifelong knowledge editing via HSIC-based dynamic layer selection and null-space constrained updates, reporting up to 14% higher average accuracy than prior methods.
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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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Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models
LLMs favor task-appropriate reasoning over conflicting instructions, yet reasoning types are linearly encoded in middle-to-late layers and can be steered to boost instruction compliance by up to 29%.
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Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models
LLMs prioritize task-appropriate reasoning over conflicting instructions, but reasoning types are linearly encoded in middle-to-late layers, allowing activation steering to raise instruction compliance by up to 29%.
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Don't Use a Cannon to Kill a Fly: Lightweight Model Editing for LLMs to Correct Deprecated API Recommendations
AdaLoRA-L restricts edits to API-specific layers and raises specificity by 33–836% (relative) on a new 3,000+ instance benchmark while staying close to AdaLoRA's effectiveness.
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FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing
FPEdit uses knowledge editing with a promote-suppress objective to embed robust, stealthy natural-language fingerprints into LLMs, achieving 94 to 100 percent retention after fine-tuning while preserving benchmark per...
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CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance
Stability-aware rank-1 LoRA editing with degradation-debt control reduces forgetting 11–17% and raises test success to 28% on 4-bit OpenLLaMA-3B UK finance facts.
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Robust Knowledge Editing via Explicit Reasoning Chains for Distractor-Resilient Multi-Hop QA
Teaching an LLM to emit a fixed four-stage reasoning chain during fine-tuning makes single-pass multi-hop knowledge editing robust to distractor facts.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
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