title: The Mirage of Model Editing: Revisiting Evaluation in the Wild
summary: Despite near-perfect results in artificial evaluations, the effectiveness of
  model editing in real-world applications remains unexplored. To bridge this
  gap, we propose to study model editing in question answering (QA) by
  establishing a rigorous evaluation practice to assess the effectiveness of
  editing methods in correcting LLMs' errors. It consists of QAEdit, a new
  benchmark derived from popular QA datasets, and a standardized evaluation
  framework. Our single editing experiments indicate that current editing methods
  perform substantially worse than previously reported (38.5% vs. ~96%). Through
  module analysis and controlled experiments, we demonstrate that this
  performance decline stems from issues in evaluation practices of prior editing
  research. One key issue is the inappropriate use of teacher forcing in testing
  prevents error propagation by feeding ground truth tokens (inaccessible in
  real-world scenarios) as input. Furthermore, we simulate real-world deployment
  by sequential editing, revealing that current approaches fail drastically with
  only 1000 edits. Our analysis provides a fundamental reexamination of both the
  real-world applicability of existing model editing methods and their evaluation
  practices, and establishes a rigorous evaluation framework with key insights to
  advance reliable and practical model editing research.
paper_authors._id: 67b47dd2e638b35196b8e014
paper_authors.name: Wanli Yang
paper_authors._id: 67b47dd2e638b35196b8e015
paper_authors.name: Fei Sun
paper_authors._id: 67b47dd2e638b35196b8e016
paper_authors.name: Jiajun Tan
paper_authors._id: 67b47dd2e638b35196b8e017
paper_authors.name: Xinyu Ma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-19T09:04:36.300Z
paper_authors._id: 62cd2f13979d883655cd5377
paper_authors.avatarUrl: /avatars/400c252d20d68aca56e0d0280498ce17.svg
paper_authors.fullname: Xinyu Ma
paper_authors.type: user
paper_authors.user: xyma
paper_authors._id: 67b47dd2e638b35196b8e018
paper_authors.name: Qi Cao
paper_authors._id: 67b47dd2e638b35196b8e019
paper_authors.name: Dawei Yin
paper_authors._id: 67b47dd2e638b35196b8e01a
paper_authors.name: Huawei Shen
paper_authors._id: 67b47dd2e638b35196b8e01b
paper_authors.name: Xueqi Cheng
paper_title: The Mirage of Model Editing: Revisiting Evaluation in the Wild
paper_summary: Despite near-perfect results in artificial evaluations, the effectiveness of
  model editing in real-world applications remains unexplored. To bridge this
  gap, we propose to study model editing in question answering (QA) by
  establishing a rigorous evaluation practice to assess the effectiveness of
  editing methods in correcting LLMs' errors. It consists of QAEdit, a new
  benchmark derived from popular QA datasets, and a standardized evaluation
  framework. Our single editing experiments indicate that current editing methods
  perform substantially worse than previously reported (38.5% vs. ~96%). Through
  module analysis and controlled experiments, we demonstrate that this
  performance decline stems from issues in evaluation practices of prior editing
  research. One key issue is the inappropriate use of teacher forcing in testing
  prevents error propagation by feeding ground truth tokens (inaccessible in
  real-world scenarios) as input. Furthermore, we simulate real-world deployment
  by sequential editing, revealing that current approaches fail drastically with
  only 1000 edits. Our analysis provides a fundamental reexamination of both the
  real-world applicability of existing model editing methods and their evaluation
  practices, and establishes a rigorous evaluation framework with key insights to
  advance reliable and practical model editing research.
paper_ai_keywords: teacher forcing ground truth tokens sequential editing model editing question answering (QA) LLMs (Large Language Models) QAEdit benchmark evaluation framework error correction