Benchmarking Large Language Models for Biomedical Relation Extraction
Extracting SNP-phenotype associations from biomedical literature is vital but challenging. We benchmarked diverse NLP models, including MLMs, hybrid architectures, and state-of-the-art LLMs (Gemini 2.0, OpenAI O-series, Qwen, Mistral), on the SNPPhenA corpus across three tasks: sentence-level, abstract-level, and association strength classification. OpenAI O1 achieved state-of-the-art (SOTA) results using few-shot learning for non-finetuned sentence-level classification (F1 0.89) and established a new SOTA for abstract-level classification (F1 0.82). Association strength classification proved difficult, though fine-tuned Gemini 2.0 Pro performed best (F1 0.60) in the first LLM evaluation ...
| Authors | Claudiu Creanga, Teodor Marchitan, Liviu P. Dinu |
|---|---|
| Primary category | cs.CL |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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