The Mirage of Model Editing: Revisiting Evaluation in the Wild
Authors: name: Wanli Yang, name: Fei Sun, name: Jiajun Tan, name: Xinyu Ma, name: Qi Cao, name: Dawei Yin, name: Huawei Shen, name: Xueqi Cheng

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.