Editing the Mind of Giants: An In-Depth Exploration of Pitfalls of Knowledge Editing in Large Language Models (2024)

Abstract: Knowledge editing is a rising technique for efficiently updating factual knowledge in large language models (LLMs) with minimal alteration of parameters. However, recent studies have identified side effects, such as knowledge distortion and the deterioration of general abilities, that have emerged after editing. Despite these findings, evaluating the pitfalls of knowledge editing often relies on inconsistent metrics and benchmarks, lacking a uniform standard. In response, this survey presents a comprehensive study of these side effects, providing a unified perspective on the challenges of knowledge editing in LLMs by conducting experiments with consistent metrics and benchmarks. Additionally, we review related works and outline potential research directions to address these limitations. Our survey highlights the limitations of current knowledge editing methods, emphasizing the need for a deeper understanding of the inner knowledge structures of LLMs and improved knowledge editing methods. To foster future research, we have released the complementary materials publicly (https://github.com/MiuLab/EditLLM-Survey).

## introduction recent advancements in large language models (llms) have significantly improved nlp applications, enabling llms to understand and generate language at a human-like level. however, the mechanisms of knowledge storage in llms remain unclear, raising concerns about the reliability of their output, particularly in applications like chatbots. to address these issues, researchers have explored various methods. traditional methods like fine-tuning, continual learning, and retraining are computationally expensive and may degrade llm performance. knowledge editing has emerged as a promising alternative, offering efficient adjustments with minimal computational costs and fewer alterations (cao 1 https://github.com/miulab/editllm-survey * equal contribution. the subsequent sections dive into three key challenges: generalization issues (section 3.1), locality issues (section 3.2), and deterioration of general llm abilities (section 3.3). @xcite @xcite @xcite @xcite @xcite @xcite @xcite . this method allows precise llms refinement, enhancing their practical and reliable use in real-world applications. knowledge editing can be divided into two main categories: parameter-modifying and parameterpreserving. both aim to refine llm knowledge efficiently while avoiding the drawbacks of previous tuning methods @xcite . parametermodifying methods, including meta-learning @xcite @xcite and locate-and-edit techniques @xcite @xcite @xcite , strive to update model parameters effectively. by contrast, parameter-preserving methods introduce external components, like knowledge bases @xcite or extra model parameters @xcite @xcite to maintain the integrity of pre-trained llms while updating their knowledge. despite the success of knowledge editing, challenges remain. recent studies have revealed side effects that can harm the general capabilities and intrinsic structures of llms. we categorize these pitfalls into three main areas: (1) the inability to perform logical inference @xcite @xcite @xcite , (2) the unintended modification of non-target knowledge @xcite @xcite @xcite , and (3) the deterioration of general llm abilities @xcite @xcite . although various side effects have been identified, they are evaluated using inconsistent metrics and benchmarks in different studies, which lack a uniform standard. as a result, this survey aims to provide a comprehensive overview of the current issues in the knowledge editing paradigm and to establish a fair platform for comparing the side effects of different editing methods. additionally, we encourage further investigation into the pitfalls and underlying knowledge structures of llms. a brief overview of the discussed pitfalls is shown in figure 1 . this paper is organized as follows: section 2 introduces the definition and methods of knowledge editing. section 3 discusses current challenges and corresponding benchmarks. in section 4, we present experimental results evaluating different editing methods. finally, section 5 explores related studies and future research directions. we summarize our contributions as follows: ## challenges of knowledge editing while knowledge editing methods have been extensively researched, comprehensive studies on related challenges are lacking. in this section, we discuss the pitfalls of knowledge editing from three perspectives: inability to logically infer and robustly generalize (section 3.1), unintended alteration of non-target knowledge (section 3.2), and deterioration of general llm abilities (section 3.3). ## experiments the experiments are done to evaluate robust generalization and locality (section 4.1.1 as well as deterioration of general llm abilities (section 4.1.2 across different editing methods. ## conclusion although model editing techniques appear promising for cost-effectively updating knowledge, they still have significant pitfalls. current editing methods often struggle with making logical inferences based on the edited facts, introducing unintended alterations of non-target knowledge and deterioration in model performance, particularly with parametermodified methods. by harnessing information retrieval techniques and delving into how models store and process knowledge, deviations in model abilities can be mitigated, and the controllability of edited facts can be enhanced, ultimately leading to greater robustness. we hope our work illuminates potential directions for future improvements in knowledge editing.