Is Fine-Tuning an Effective Solution? Reassessing Knowledge Editing for Unstructured Data

ACL ARR 2025 May Submission2692 Authors

Abstract:

Unstructured Knowledge Editing (UKE) is crucial for updating the relevant knowledge of large language models (LLMs). It focuses on unstructured inputs, such as long or free-form texts, which are common forms of real-world knowledge. Although previous studies have proposed effective methods and tested them, some issues exist: (1) Lack of Locality evaluation for UKE, and (2) Abnormal failure of fine-tuning (FT) based methods for UKE.
To address these issues, we first construct two datasets, UnKEBench-Loc and AKEW-Loc (CF), by extending two existing UKE datasets with locality test data from the unstructured and structured views. This enables a systematic evaluation of the Locality of post-edited models.
Furthermore, we identify four factors that may affect the performance of FT-based methods. Based on these factors, we conduct experiments to determine how the well-performing FT-based methods should be trained for the UKE task, providing a training recipe for future research. Our experimental results indicate that the FT-based method with the optimal setting (FT-UKE) is surprisingly strong, outperforming the existing state-of-the-art (SOTA).
% , in terms of both existing metrics and average metrics that consider the newly introduced metric, Locality.
In batch editing scenarios, FT-UKE shows strong performance as well, with its advantage over SOTA methods increasing as the batch size grows, expanding the average metric lead from +6.78\% to +10.80\%. Our code and data will be released on Github.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: model editing

Contribution Types: Reproduction study, Data resources

Languages Studied: English

Submission Number: 2692