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Editing Large Language Models: Problems, Methods, and Opportunities
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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 context. Code and datasets are available at https://github.com/zjunlp/EasyEdit.
Forward citations
Cited by 10 Pith papers
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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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Efficient Knowledge Editing via Minimal Precomputation
Precomputing only a few thousand hidden vectors instead of 44 million is enough for MEMIT, ROME, and EMMET editing to match full-precomputation scores on CounterFact.
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One for All: Update Parameterized Knowledge Across Multiple Models
One fine-tuned small model plus an ensemble step can update a fact across multiple large language models with a single edit, outperforming separate per-model editing.
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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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PREE: Towards Harmless and Adaptive Fingerprint Editing in Large Language Models via Knowledge Prefix Enhancement
PREE edits a tiny fraction of an LLM's weights so the model answers fake facts when triggered by specially selected prefix phrases, enabling robust ownership verification.
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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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Concept Incongruence: An Exploration of Time and Death in Role Playing
LLMs asked to role-play dead historical figures rarely abstain from answering post-death questions, and their factual accuracy drops due to poorly encoded death states and role-playing-induced shifts in temporal repre...
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Understanding Knowledge Transferability for Transfer Learning: A Survey
A survey that classifies transferability metrics by knowledge modality (dataset vs. model) and granularity (task vs. instance), with a theoretical primer and applications to eight learning paradigms.
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