Robust evaluation of PLMs for adaptive immune receptor tasks

Develop robust, standardized evaluation frameworks and benchmarks to assess both performance and learned representations of protein language models across diverse tasks specific to adaptive immune receptors, ensuring comprehensive and reliable comparisons across models and applications.

Background

The paper surveys a rapid proliferation of both general and immune-receptor specific protein LLMs and notes heterogeneous evaluation practices across different tasks and datasets. Although individual studies compare performance on particular benchmarks, the authors emphasize that a unified, rigorous approach to assessing both performance and representation across the spectrum of adaptive immunity tasks is lacking.

This open problem highlights the need for standardized metrics and methodologies to compare models on receptor-specific tasks, such as antigen binding and paratope prediction, and on repertoire-level analyses, ensuring fair and reproducible assessments.