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Memory-Based Model Editing at Scale
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Even the largest neural networks make errors, and once-correct predictions can become invalid as the world changes. Model editors make local updates to the behavior of base (pre-trained) models to inject updated knowledge or correct undesirable behaviors. Existing model editors have shown promise, but also suffer from insufficient expressiveness: they struggle to accurately model an edit's intended scope (examples affected by the edit), leading to inaccurate predictions for test inputs loosely related to the edit, and they often fail altogether after many edits. As a higher-capacity alternative, we propose Semi-Parametric Editing with a Retrieval-Augmented Counterfactual Model (SERAC), which stores edits in an explicit memory and learns to reason over them to modulate the base model's predictions as needed. To enable more rigorous evaluation of model editors, we introduce three challenging language model editing problems based on question answering, fact-checking, and dialogue generation. We find that only SERAC achieves high performance on all three problems, consistently outperforming existing approaches to model editing by a significant margin. Code, data, and additional project information will be made available at https://sites.google.com/view/serac-editing.
Forward citations
Cited by 6 Pith papers
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MEMORA: Embodied Action Memory from Egocentric Videos for Reasoning and Planning
A typed, editable memory built from egocentric video improves memory-grounded question answering and out-of-distribution robot planning over flat-text and graph baselines.
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Implicit Reasoning Steering via Concept Chaining
Reinforcement-learning-optimized concept-chain paragraphs covertly steer language-model multiple-choice preferences after continued pretraining, with far lower detectability than direct paraphrases.
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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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Improving LLM-Based Fault Localization with External Memory and Project Context
MemFL gives an LLM static project summaries and dynamic debugging tips, and reports a 12.7% Top-1 accuracy gain over LLM fault localization baselines on Defects4J with lower time and cost.
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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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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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