Neighboring Perturbations of Knowledge Editing on Large Language Models (2401.17623v2)

Abstract: Despite their exceptional capabilities, LLMs are prone to generating unintended text due to false or outdated knowledge. Given the resource-intensive nature of retraining LLMs, there has been a notable increase in the development of knowledge editing. However, current approaches and evaluations rarely explore the perturbation of editing on neighboring knowledge. This paper studies whether updating new knowledge to LLMs perturbs the neighboring knowledge encapsulated within them. Specifically, we seek to figure out whether appending a new answer into an answer list to a factual question leads to catastrophic forgetting of original correct answers in this list, as well as unintentional inclusion of incorrect answers. A metric of additivity is introduced and a benchmark dubbed as Perturbation Evaluation of Appending Knowledge (PEAK) is constructed to evaluate the degree of perturbation to neighboring knowledge when appending new knowledge. Besides, a plug-and-play framework termed Appending via Preservation and Prevention (APP) is proposed to mitigate the neighboring perturbation by maintaining the integrity of the answer list. Experiments demonstrate the effectiveness of APP coupling with four editing methods on four LLMs. The code and data are available at https://github.com/mjy1111/PEAK.

Analyzing Neighboring Perturbations of Knowledge Editing in LLMs

The paper entitled "Neighboring Perturbations of Knowledge Editing on LLMs" embarks on an exploratory investigation of the effects surrounding knowledge editing in LLMs, a relatively nascent but urgent area of machine learning research. As these models become more omnipresent in applications, one of the exigent issues is updating them without retraining—a process both resource-intensive and time-consuming—as they exhibit outdated or erroneous knowledge. Knowledge editing emerges as a viable pathway in this context, enabling edits to model behaviors without complete retraining. However, a lacuna remains in understanding its second-order effects, specifically its impact on pertinent but non-targeted knowledge, which this paper addresses.

Core Contributions and Methods

The authors delineate two primary contributions: the proposal of a novel metric, termed additivity, and the development of a benchmark named Perturbation Evaluation of Appending Knowledge (PEAK). Additivity measures the degree of perturbation in the neighboring knowledge to newly updated facts using both relative ranking and absolute probability change metrics. Furthermore, the paper introduces a plug-and-play framework called Appending via Preservation and Prevention (APP), aimed at mitigating adverse consequences in non-targeted knowledge areas when new knowledge is appended.

The APP framework is selectively integrated with four existing editing techniques—FT, MEND, ROME, and MEMIT—across several LLMs, including GPT-2 XL, GPT-J, and LLaMA-2. The paper specifies comprehensive experiments using these models to assess the efficacy and mitigation capabilities of APP. Notably, results across multiple setups suggest that while these methods are adept at incorporating new factual information, they often significantly disrupt the probability distribution of related knowledge, leading to possible detrimental side-effects.

Numerical Insights and Implications

Numerical evaluations present several key observations. A prominent insight is that existing methods, despite their success in embedding the new target knowledge, often exacerbate vulnerability within neighboring knowledge frames, which is quantified using the Affinity Forgetting Factor (AFF) and the Additive Noising Factor (ANF). For instance, methods like MEMIT and ROME, although effective for fact updates, showed high AFF values, reflecting extensive disturbances to related information when tested across both PEAK-CF (counterfactual facts) and PEAK-T (temporal facts) datasets.

Remarkably, when APP was applied, there was a significant reduction in both AFF and ANF without compromising the efficacy of the core knowledge editing operation. This demonstrates APP's robustness in reducing unintended knowledge perturbation, serving as a potent solution to preserve model accuracy beyond the immediate scope of edits.

Future Directions in AI

This paper not only underscores the importance of precision in knowledge editing but also illuminates potential trajectories for further research. As LLMs are increasingly central to decision-making systems, understanding and managing the breadth of their knowledge is not merely advantageous but essential. The introduction of frameworks like APP could potentially streamline the integration of evolving knowledge into AI models while maintaining the fidelity and stability of pre-existing knowledge domains.

Future trajectories could emphasize expanded benchmarks encompassing diverse fact types and explore APP's scalability and adaptability across newer LLM architectures. Moreover, the paper introduces the potential for developing automated methods to detect and rectify perturbations, ensuring more resilient and reliable AI systems.

In summary, the research rigorously examines the often-overlooked collateral perturbations associated with knowledge editing in LLMs, providing valuable insights and practical methodologies to enhance the robustness of AI systems against such transformations. This work ideally positions itself as a cornerstone for ongoing refinement in knowledge manipulation techniques, aligning theoretical explorations with essential practical applications.