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arxiv: 2403.14236 · v5 · pith:DETY2BUH · submitted 2024-03-21 · cs.LG · cs.AI· cs.CL

A Unified Framework for Model Editing

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classification cs.LG cs.AIcs.CL
keywords editingbatchedmemitmodelromealgorithmsconstraintemmet
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ROME and MEMIT are largely believed to be two different model editing algorithms, with the major difference between them being the ability to perform batched edits. In this paper, we unify these two algorithms under a single conceptual umbrella, optimizing for the same goal, which we call the preservation-memorization objective. ROME uses an equality constraint to optimize this objective to perform one edit at a time, whereas MEMIT employs a more flexible least-square constraint that allows for batched edits. We generalize ROME and enable batched editing with equality constraint in the form of EMMET - an Equality-constrained Mass Model Editing algorithm for Transformers, a new batched memory-editing algorithm. EMMET can perform batched-edits up to a batch-size of 10,000, with very similar performance to MEMIT across multiple dimensions. With the introduction of EMMET, we truly unify ROME and MEMIT and show that both algorithms are equivalent in terms of their optimization objective, their abilities (singular and batched editing), their model editing performance and their limitations.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Knowledge Editing in Masked Diffusion Language Models

    cs.CL 2026-06 unverdicted novelty 7.0

    Locate-then-edit succeeds at the same early-to-mid MLP locations in masked diffusion models as in autoregressive models, but requires optimization over intermediate partial-mask states to handle multi-token targets.

  2. Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs

    cs.LG 2026-04 conditional novelty 6.0

    DECODE identifies and separately edits modality-specific neurons in MLLMs to prevent knowledge edits from reverting under unimodal queries.

  3. CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing

    cs.LG 2026-03 unverdicted novelty 6.0

    CLaRE quantifies representational entanglement in LLMs using single-layer forward activations to predict editing ripple effects, reporting 62.2% higher Spearman correlation than baselines while using 2.74x less time a...

  4. Understanding Robustness of Model Editing in Code LLMs

    cs.SE 2025-11 unverdicted novelty 6.0

    A controlled benchmark on 2040 problems reveals poor generalization and high interference in model editing for API updates in code LLMs, with many successes being workarounds rather than true migrations.