On the Robustness of Editing Large Language Models (2024)

Abstract: Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation without retraining. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI. We focus on three key research questions. RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? RQ2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? RQ3: Which knowledge features are correlated with the performance and robustness of editing? Our empirical studies uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively.

## introduction model editing is proposed to modify the knowledge memory with minimum computational cost while preserving the performance on the retained knowledge. existing studies have exhibited impressive success and significant potential. these methods can be classified into two categories. one research line relies on additional supporting modules, for example, an external memory @xcite , a hypernetwork @xcite , or a retriever @xcite . another line studies localized editing based on the interpretability of knowledge storage mechanism @xcite @xcite . these methods avoid retraining to update the model parameters and have demonstrated promising performance and efficiency. at the application level, model editing provides solutions to critical challenges in pre-training language models, such as knowledge correction, time alignment, and privacy protection @xcite @xcite @xcite . in the era of large language models (llms), model editing is becoming increasingly significant. the rich knowledge memory empowers llms to build communicative ai, where they can engage in multi-turn interactions to imitate human behaviors for communicative actions @xcite @xcite . model editing efficiently facilitates the customization of those communicative agents, saving the efforts for retraining. users can remove undesirable knowledge or even alter the "personality" of communicative ai @xcite conveniently. however, as we pursue the practical use of edited communicative ai, the robustness of model editing methods becomes a critical concern. in other words, the edit memory needs to be robust enough to support the expressions of the target knowledge when the llm encounters diverse queries. in realistic applications, such as a chatting service, the edited memory is anticipated to handle complex scenarios. motivated by the thoughts above, we put forward three novel research questions: to answer rq1, this paper begins with an experiment to show the modest robustness of the edited memory when an edited llm is asked to perform as communicative ai. we show that the edited model is prone to confusion and hallucination in the neighborhood intersections of knowledge. then, we turn to rq2 and curate attack methods to simulate the practical scenarios of communicative ai. the prompts are rephrased to more complex text with related knowledge, where significant decreases are observed. rq3 focus on the intrinsic features of knowledge. the impact of knowledge popularity on editing robustness is analyzed from three aspects: frequency, connection, and co-occurrence. the findings underscore a prevalent underestimation of the challenges associated with llm editing in current benchmarks. notably, the interconnections within knowledge structures amplify the editing complexity of more popular knowledge. as the answers to the proposed questions, the key findings are as follows: ## related work this section reviews methods and reflections on model editing, and llm-based communicative ai. ## task formulation this section presents the task formulation of our paper, where we first introduce the definition of model editing and then clarify the research focus. figure 1 shows the overview of our investigation. definition. the task definition of model editing follows the relational triplet extraction @xcite . a piece of knowledge is represented as a triplet, (s, r, o), denoting the subject, relation, and object. model editing aims to change some pieces of knowledge memory. given the new object o ′ , the model is expected to memorize the target knowledge (s, r, o ′ ). the concept editing scope is essential as each triplet can be implied by various expressions @xcite . we denote the direct prompt entailing (s, r) as x, its semantically rele-vant neighbors as {x e }, and irrelevant neighbors as {x loc }. an optimal edit distinguishes the editing scope. the edit should change the model behaviors on x and {x e } according to o ′ , while maintaining other memory and responses to {x loc }. focus. this study reassesses the robustness of the edited knowledge memory in realistic scenarios by novel methods. without loss of generality, we aim to reveal risks under the primary edit setup. experiments follow the original definition of the fact edit with triplet representation and consider a single edit for one run. previous studies involving side effects, general ability decrease, and complex knowledge editing are not the focus of our work. ## rq1: edited llm as communicative ai this section identifies the potential risks associated with the practical application of edited llms (rq1), especially as a communicative ai agent. ## rq3: knowledge popularity affecting editing robustness besides the extrinsic effects like various inputs, this section studies rq3, the influence of intrinsic knowledge features on editing, especially the popularity. ## potential mitigation our work could suggest promising directions for improving the editing robustness as follows. ## conclusion this paper systematically studies recent model editing methods under the situation of practical use and raises concerns about their robustness. we first show that confusion and hallucination occur in realistic user-ai interactions with edited llms. besides, we rephrase the prompts by adding context and changing the question format to attack editing, demonstrating the vulnerability of target knowledge. for more analysis, we propose three knowledge popularity measurements and show that popular knowledge is memorized better, easier to recall, and harder to robustly edit for llms. editing methods have shown impressive success, while they can be problematic in practical situations because of existing robustness deficiencies. more importantly, this paper calls for effort on this inspiring research topic and underscores the collective focus on improving editing robustness for further application.