MULFE : A Multi-Level Benchmark for Free Text Model Editing (2024)

Abstract: Adjusting the outdated behaviors of large langugae models (LLMs) after deployment remains a significant challenge. It motivates the model editing research, which is however mainly explored in a restricted task form with triple-based edit requests. Recent works have initiated a transition to a more practical and unified editing task that takes free-form text as edit requests. However, there are gaps in nuanced benchmark designs and re-evaluation of existing methods. To bridge the gaps, we introduce a multi-level benchmark for free text model editing (MULFE). The benchmark categorizes probe queries into three levels of generalization, ranging from basic literal memory to deeper understanding and reasoning. Based on the benchmark, we conduct extensive experiments across various base models, edit sizes, and editing methods, including adaptations of mainstream locate-and-edit and hypernetwork methods. The results highlight the inconsistent behaviors of edited models on different generalization levels. Higher-level generalization remains a significant challenge. Based on the findings, we propose SIDE, a simple yet effective method based on in-context distillation to enhance the generalization performance. The benchmark dataset and evaluation scripts are publicly available at http://github.com/wchrepo/mulfe.

## introduction large language models (llms) have showcased impressive capabilities in comprehending human language @xcite as well as vast parametric knowledge obtained from large corpora @xcite . however, as new information keeps emerging, adjusting the outdated behaviors of llms after deployment remains a significant challenge. unlike humans, who can naturally assimilate new knowledge from new text and did ginny & georgia premiere before 2020? ## conclusion this work presents the mulfe benchmark for free text model editing and provides extensive empirical results across different settings. for the take-home message, we highlight the findings as follows. based on the findings, we propose a simple yet effective method side, which shows substantial improvement in the higher generation levels. however, much work is needed for an all-round solution. to sum up, free text model editing is a practical but largely unsolved task. the evaluation benchmark and baseline methods proposed in this paper can facilitate further work in this field.