Evaluating the Ripple Effects of Knowledge Editing in Language Models (2307.12976v2)

Abstract: Modern LLMs capture a large body of factual knowledge. However, some facts can be incorrectly induced or become obsolete over time, resulting in factually incorrect generations. This has led to the development of various editing methods that allow updating facts encoded by the model. Evaluation of these methods has primarily focused on testing whether an individual fact has been successfully injected, and if similar predictions for other subjects have not changed. Here we argue that such evaluation is limited, since injecting one fact (e.g. Jack Depp is the son of Johnny Depp'') introduces aripple effect'' in the form of additional facts that the model needs to update (e.g.``Jack Depp is the sibling of Lily-Rose Depp''). To address this issue, we propose a novel set of evaluation criteria that consider the implications of an edit on related facts. Using these criteria, we then construct RippleEdits, a diagnostic benchmark of 5K factual edits, capturing a variety of types of ripple effects. We evaluate prominent editing methods on RippleEdits, showing that current methods fail to introduce consistent changes in the model's knowledge. In addition, we find that a simple in-context editing baseline obtains the best scores on our benchmark, suggesting a promising research direction for model editing.

Evaluating the Ripple Effects of Knowledge Editing in LLMs

The paper "Evaluating the Ripple Effects of Knowledge Editing in LLMs" addresses significant challenges within the field of modern LLMs (LMs) concerning the accuracy and update of factual knowledge encoded in these models. It identifies a critical gap in existing evaluation methods for knowledge editing (KE) and proposes a new approach to assess the broader implications of making specific factual updates.

Problem Identification and Motivation

LLMs are inherent repositories of extensive factual information, which are pivotal for various downstream applications. However, the factual data can sometimes be erroneous or outdated, which compromises the efficacy and reliability of these models. This has spurred the development of KE techniques aimed at rectifying incorrect or obsolete knowledge in LMs. The current evaluation protocols primarily focus on verifying whether a specific fact is correctly injected and ensuring that unrelated facts remain unchanged. However, these methods do not sufficiently address the cascading effects on logically connected information that a single factual change could trigger within the model's knowledge.

Methodological Innovation

The authors introduce the concept of "ripple effects" in knowledge editing, which refers to secondary updates required in logically related facts due to a primary factual change. The paper emphasizes that modifying one fact can necessitate cascading changes across other related facts—akin to a ripple effect. For instance, successfully updating the fact "Jack Depp is the son of Johnny Depp" should also ensure the accuracy of related facts such as "Jack Depp is the sibling of Lily-Rose Depp."

RippleEdits Benchmark: To facilitate the comprehensive evaluation of such ripple effects, the authors develop RippleEdits, a diagnostic benchmark comprising 5,000 factual edits. This benchmark encompasses multiple test queries designed to assess whether models integrate factual updates coherently into their existing knowledge structure.

Evaluation Criteria

The benchmark introduces several evaluation criteria, including:

• Logical Generalization: Verifying that edits remain consistent with logical properties such as symmetry or transitivity of the relations involved.
• Compositionality: Checking for correct inference when edited facts are combined with other facts, involving complex relationships.
• Subject Aliasing: Ensuring edits are correctly reflected across different aliases of a subject.
• Preservation: Maintaining accuracy in the presence of multiple objects associated with a relation, ensuring added edits do not alter unrelated objects.
• Relation Specificity: Verifying that only logically connected facts are altered while unrelated facts remain intact.

Experimental Findings

The authors evaluate several popular KE methods using the RippleEdits benchmark, including MEND, ROME, and MEMIT, across different models like GPT-2, GPT-J, GPT-NeoX, and LLaMA. A notable finding is that existing methods, although able to make targeted edits, generally miss the extended ripple effects, failing to propagate modifications consistently across related facts.

Moreover, a basic in-context learning editing approach demonstrated superior performance on the benchmark, suggesting promising directions for future research. This baseline relies on conditioning the model's generation rather than altering its parameters, indicating that structure-preserving strategies might be advantageous.

Implications and Future Directions

This paper's insights are crucial for enhancing the reliability and utility of LMs in real-world applications. By highlighting the shortcomings in current KE methods regarding ripple effects, the paper informs future research to develop more holistic and effective editing techniques that maintain the logical coherence of model knowledge post-editing. It suggests that in-context methods hold potential for improving the integration of new knowledge more dynamically without directly modifying model parameters.

Future research could explore enhancing the scalability of knowledge updates, investigating the effects of batch-editing multiple related facts simultaneously, and examining the architecture-specific mechanisms that facilitate or hinder the propagation of edits within LMs. The RippleEdits benchmark provides a structured framework for such explorations, paving the way for more accurate and trustable LLM applications.