Can We Edit LLMs for Long-Tail Biomedical Knowledge? A New Study Reveals the Challenges

Large language models (LLMs) are increasingly being used in the biomedical domain, but their ability to handle rare or infrequent knowledge—known as long-tail knowledge—remains a significant challenge. A new study from researchers at the University of Glasgow explores whether knowledge editing, a technique for updating LLMs by modifying their internal knowledge, can effectively improve their performance on long-tail biomedical facts. The findings, published in a recent arXiv preprint, reveal both promise and limitations.

The Problem with Long-Tail Knowledge

Biomedical data is inherently long-tailed: a small fraction of knowledge (like common diseases) appears frequently, while the vast majority (like rare conditions) appears only a handful of times. For example, "Type 1 Diabetes" is mentioned in over 100,000 PubMed papers, while "Evans Syndrome" appears in just 23. This imbalance makes it difficult for LLMs to learn and retain rare but critical information during pre-training.

The study probes whether knowledge editing—methods like ROME, MEMIT, and MEND that surgically update model weights—can bridge this gap. The researchers extracted over 100,000 biomedical knowledge triples from SNOMED CT, mapped them to PubMed documents, and classified them by frequency. They then tested how well LLMs could recall these facts before and after editing.

Key Findings

1. LLMs Struggle with Long-Tail Knowledge

• Even biomedical-specific models like BioMedLM and BioGPT perform significantly worse on rare facts. For instance, BioMedLM’s accuracy drops by 22.86% on long-tail knowledge compared to popular knowledge.
• The issue is exacerbated by the prevalence of "one-to-many" relationships (e.g., a disease linked to multiple treatments), which make up 90.4% of long-tail triples.

1. Editing Helps, But Not Enough

• Knowledge editing improves performance, with ROME boosting BioMedLM’s accuracy on long-tail facts by 52.08%. However, edited models still lag behind on rare knowledge compared to common facts.
• Edited LLMs can memorize the form of long-tail facts but struggle to generalize them, especially for one-to-many relationships.

1. One-to-Many Knowledge Is the Bottleneck

• The high frequency of one-to-many relationships in long-tail data limits editing effectiveness. For example, after editing, the accuracy gap between one-to-one and one-to-many knowledge narrows but persists.

Why This Matters

As LLMs are increasingly used in clinical settings—for diagnosis, treatment recommendations, and literature review—their inability to reliably handle rare knowledge poses risks. The study highlights the need for specialized editing techniques tailored to biomedical long-tail scenarios, particularly for complex, multi-answer relationships.

The Path Forward

The authors suggest future work should focus on:

• Better handling of one-to-many knowledge, perhaps by refining how edits are applied to avoid overwriting existing associations.
• Sentence-level co-occurrence analysis to improve the precision of long-tail knowledge extraction.
• Domain-specific editing methods that account for the unique structure of biomedical data.

For now, the takeaway is clear: while knowledge editing can enhance LLMs’ grasp of rare biomedical facts, significant challenges remain. The full paper, including datasets and code, is available on GitHub.