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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cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data.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 14 Pith papers
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Towards Efficient and Effective Alignment of Large Language Models
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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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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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On the Adaptive Psychological Persuasion of Large Language Models
An adaptive preference-optimization method helps LLM persuaders choose among 11 psychological strategies, improving persuasion success on counterfactual facts while preserving general capability.
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COMPKE: Complex Question Answering under Knowledge Editing
COMPKE is a new benchmark with 11,924 complex questions that tests knowledge editing through one-to-many relations and logical operations, where existing editing methods often fail.
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Context-Robust Knowledge Editing for Language Models
Preceding distractor contexts cause most knowledge-editing methods to fall back to old facts, and CoRE's variance-regularized edit reduces this failure.
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Adaptive Detoxification: Safeguarding General Capabilities of LLMs through Toxicity-Aware Knowledge Editing
ToxEdit uses a hidden-state toxicity classifier to route unsafe queries through an edited feed-forward network, preserving normal responses on safe queries.
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REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing
REACT edits LLM facts by adding a learned belief-shift vector to hidden states only when a classifier decides the context is edit-relevant, reducing overfitting on EVOKE while keeping balanced editing metrics.
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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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CaseEdit: Enhancing Localized Commonsense Reasoning via Null-Space Constrained Knowledge Editing in Small Parameter Language Models
CaseEdit supplies 900 household-object commonsense edits with 3,600 multiple-choice questions and reports that AlphaEdit beats ROME, MEND, MEMIT, and MEMIT-CSK at preserving unrelated knowledge in a 3B model.
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Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms
STA selects sparse autoencoder features by activation amplitude and frequency to build steering vectors that improve LLM safety control over prompt engineering and standard steering.
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A block-localizing fine-tuning method for gender debiasing is presented, but its stated loss is inconsistent with its reported behavior and the evaluation tables contain duplicate rows.
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