Can We Edit LLMs for Long-Tail Biomedical Knowledge?
Journal:
arXiv
Published Date:
Apr 14, 2025
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.
