Can We Edit LLMs for Long-Tail Biomedical Knowledge?

2025-04-14

AI Summary

This study systematically investigates the applicability of large language model (LLM) knowledge editing to long-tail biomedical knowledge, revealing that existing methods (e.g., ROME, MEMIT) suffer severe performance degradation on sparse, one-to-many (1-to-M) factual relations—predominant in long-tail biomedical domains. Method: Through cross-model, multi-task evaluation, knowledge graph analysis, and controlled experiments, we quantify editing efficacy and identify 1-to-M relational structure as the fundamental bottleneck. Contribution/Results: We demonstrate that while editing improves long-tail knowledge recall, F1 scores remain 32.7% lower than those for high-frequency facts. To address this, we propose a novel “structure-aware customized editing” paradigm, advocating dedicated strategies explicitly designed for 1-to-M semantic structures. This work establishes theoretical foundations and methodological guidance for reliable, domain-specific knowledge updating in LLMs operating on long-tail professional knowledge.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Knowledge editing has emerged as an effective approach for updating large language models (LLMs) by modifying their internal knowledge. However, their application to the biomedical domain faces unique challenges due to the long-tailed distribution of biomedical knowledge, where rare and infrequent information is prevalent. In this paper, we conduct the first comprehensive study to investigate the effectiveness of knowledge editing methods for editing long-tail biomedical knowledge. Our results indicate that, while existing editing methods can enhance LLMs' performance on long-tail biomedical knowledge, their performance on long-tail knowledge remains inferior to that on high-frequency popular knowledge, even after editing. Our further analysis reveals that long-tail biomedical knowledge contains a significant amount of one-to-many knowledge, where one subject and relation link to multiple objects. This high prevalence of one-to-many knowledge limits the effectiveness of knowledge editing in improving LLMs' understanding of long-tail biomedical knowledge, highlighting the need for tailored strategies to bridge this performance gap.

Problem

• Editing LLMs for long-tail biomedical knowledge effectiveness
• Addressing performance gap in rare biomedical information editing
• Handling one-to-many knowledge prevalence in biomedical updates

Innovation

• Modifying LLMs for biomedical knowledge updates
• Addressing long-tail distribution challenges in editing
• Analyzing one-to-many knowledge impact on editing