Rebuilding ROME : Resolving Model Collapse during Sequential Model Editing
Abstract: Recent work using Rank-One Model Editing (ROME), a popular model editing method, has shown that there are certain facts that the algorithm is unable to edit without breaking the model. Such edits have previously been called disabling edits. These disabling edits cause immediate model collapse and limits the use of ROME for sequential editing. In this paper, we show that disabling edits are an artifact of irregularities in the implementation of ROME. With this paper, we provide a more stable implementation ROME, which we call r-ROME and show that model collapse is no longer observed when making large scale sequential edits with r-ROME, while further improving generalization and locality of model editing compared to the original implementation of ROME.
Paper Type: Short
Research Area: Interpretability and Analysis of Models for NLP
Research Area Keywords: knowledge tracing/discovering/inducing
Contribution Types: Model analysis & interpretability, Reproduction study, Publicly available software and/or pre-trained models
Languages Studied: English
Submission Number: 4060
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