EVEDIT: Event-based Knowledge Editing with Deductive Editing Boundaries (2402.11324v1)

Abstract: The dynamic nature of real-world information necessitates efficient knowledge editing (KE) in LLMs for knowledge updating. However, current KE approaches, which typically operate on (subject, relation, object) triples, ignore the contextual information and the relation among different knowledge. Such editing methods could thus encounter an uncertain editing boundary, leaving a lot of relevant knowledge in ambiguity: Queries that could be answered pre-edit cannot be reliably answered afterward. In this work, we analyze this issue by introducing a theoretical framework for KE that highlights an overlooked set of knowledge that remains unchanged and aids in knowledge deduction during editing, which we name as the deduction anchor. We further address this issue by proposing a novel task of event-based knowledge editing that pairs facts with event descriptions. This task manifests not only a closer simulation of real-world editing scenarios but also a more logically sound setting, implicitly defining the deduction anchor to address the issue of indeterminate editing boundaries. We empirically demonstrate the superiority of event-based editing over the existing setting on resolving uncertainty in edited models, and curate a new benchmark dataset EvEdit derived from the CounterFact dataset. Moreover, while we observe that the event-based setting is significantly challenging for existing approaches, we propose a novel approach Self-Edit that showcases stronger performance, achieving 55.6% consistency improvement while maintaining the naturalness of generation.

Event-Based Knowledge Editing with Deductive Editing Boundaries in LLMs

Introduction to Event-Based Knowledge Editing

Knowledge editing (KE) in LLMs has emerged as a critical area of research, aiming to enhance models by updating their knowledge base. Traditional KE methods, mainly focusing on updating single (subject, relation, object) triples, often disregard contextual information and inter-knowledge relationships. This approach can create uncertain editing boundaries, leaving models unable to reliably answer queries post-edit, introducing a challenge termed as the editing boundary problem. The paper introduces a theoretical framework emphasizing a previously overlooked set of knowledge—deduction anchors—and proposes event-based knowledge editing as a solution, showcasing its effectiveness through a novel benchmark dataset named E V E DIT.

Theoretical Analysis and Methodological Approach

Fallacies in Current Knowledge Editing Methods

The paper identifies two significant fallacies in current knowledge editing practices: the No-Anchor Fallacy and the Max-Anchor Fallacy. It demonstrates theoretically and empirically how these fallacies lead to increased uncertainty within edited models, undermining the quality of the edits.

Introducing Deduction Anchors and Event-Based Editing

Expanding on the foundational concepts of deduction anchors and editing boundaries, this research proposes integrating event descriptions with fact updates. Event-based edits logically encompass both the facts and their contextual underpinnings, offering a more comprehensive editing approach that mitigates the issues of indeterminate boundaries.

Event-Based Knowledge Editing Benchmark: E V E DIT

The paper presents E V E DIT, a benchmark dataset created to systematically evaluate the performance of event-based edits versus traditional triple-based edits. This new benchmark demonstrates the superiority of event-based knowledge editing in preserving model certainty and naturalness of generation post-edit.

Evaluation and Results

Self-Edit, a novel methodology developed for the event-based editing task, outperforms existing approaches, achieving a 55.6% consistency improvement while maintaining the naturalness of generation. The paper also highlights the challenges faced by current methods when applied to event-based edits, further supporting the necessity of this new editing paradigm.

Implications and Future Directions

The research underscores the need for approaches that consider the broader context and the interconnectedness of knowledge for effective model updating. It opens up avenues for future research in knowledge editing, particularly in exploring more nuanced and logical methods of model modification. Moreover, it calls for advancements in editing techniques that can seamlessly incorporate events, considering not only the factual accuracy but also the model's ability to reason over edited knowledge.

Conclusion

This paper marks a significant step forward in addressing the challenges of knowledge editing in LLMs by introducing a theoretically grounded framework and practical solution through event-based knowledge editing. The proposed methods, substantiated by robust evaluation benchmarks, pave the way for more logical and context-aware model updating processes, setting a new standard for future research in the field.