Papers › Evaluation of large language model performance on the Biomedical Language...
Evaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark
Hui Feng, Francesco Ronzano, Jude LaFleur, Matthew Garber, Rodrigo de Oliveira, Kathryn Rough, Katharine Roth, Jay Nanavati, Khaldoun Zine El Abidine, Christina Mack
Background The ability of large language models (LLMs) to interpret and generate human-like text has been accompanied with speculation about their application in medicine and clinical research. There is limited data available to inform evidence-based decisions on the appropriateness for specific use cases. Methods We evaluated and compared four general-purpose LLMs (GPT-4, GPT-3.5-turbo, Flan-T5-XXL, and Zephyr-7B-Beta) and a healthcare-specific LLM (MedLLaMA-13B) on a set of 13 datasets – referred to as the Biomedical Language Understanding and Reasoning Benchmark (BLURB) – covering six commonly needed medical natural language processing tasks: named entity recognition (NER); relation extraction; population, interventions, comparators, and outcomes (PICO); sentence similarity; document classification; and question-answering. All models were evaluated without modification. Model performance was assessed according to a range of prompting strategies (formalised as a systematic, reusable prompting framework) and relied on the standard, task-specific evaluation metrics defined by BLURB. Results Across all tasks, GPT-4 outperformed other LLMs, followed by Flan-T5-XXL and GPT-3.5-turbo, then Zephyr-7b-Beta and MedLLaMA-13B. The most performant prompts for GPT-4 and Flan-T5-XXL both outperformed the previously-reported best results for the PubMedQA task. The domain-specific MedLLaMA-13B achieved lower scores for most tasks except for question-answering tasks. We observed a substantial impact of strategically editing the prompt describing the task and a consistent improvement in performance when including examples semantically similar to the input text in the prompt. Conclusion These results provide evidence of the potential LLMs may have for medical application and highlight the importance of robust evaluation before adopting LLMs for any specific use cases. Continuing to explore how these emerging technologies can be adapted for the healthcare setting, paired with human expertise, and enhanced through quality control measures will be important research to allow responsible innovation with LLMs in the medical area.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Named Entity Recognition (NER) | NCBI-disease | GPT-4 | F1 | 65.98 | #25 of 26 | Archive leaderboard | report |
| Question Answering | BLURB | GPT-4 | Accuracy | 80.56 | #3 of 4 | Archive leaderboard | report |
| Question Answering | BioASQ | GPT-4 | Accuracy | 85.71 | #6 of 7 | Archive leaderboard | report |
| Question Answering | PubMedQA | Flan-T5-XXL | Accuracy | 76.80 | #13 of 30 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections