Can Large Language Models “Read” Biological Sequences? A Systematic Evaluation of In-Context Learning for Antibody Characterization

Abstract excerpt

Large language models (LLMs) can learn new tasks by in-context learning (ICL), but it is unknown whether this ability reliably transfers to biological sequence classification. Here, we systematically evaluate how demonstration selection, shot count, and prompting strategies affect performance across 20 general-purpose LLMs. Using antibody characterization as a representative test case, we compare zero-shot, few-sh...

Topics

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• Machine Learning in Bioinformatics
• Monoclonal and Polyclonal Antibodies Research
• RNA and protein synthesis mechanisms
• T-cell and B-cell Immunology
• vaccines and immunoinformatics approaches

Identifiers and source

Literature Corpus work

4dd1c48e-6574-54e2-9b9d-4266c8ebf354

DOI

10.1101/2025.02.11.637772