@inproceedings{jararweh-etal-2025-protein2text,
    title = "{P}rotein2{T}ext: Resampling Mechanism to Translate Protein Sequences into Human-Interpretable Text",
    author = "Jararweh, Ala  and
      Macaulay, Oladimeji  and
      Arredondo, David  and
      Hu, Yue  and
      Tafoya, Luis E  and
      Virupakshappa, Kushal  and
      Sahu, Avinash",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.68/",
    pages = "918--937",
    ISBN = "979-8-89176-194-0",
    abstract = "Proteins play critical roles in biological systems, yet 99.7{\%} of over 227 million known protein sequences remain uncharacterized due to the limitations of experimental methods. To assist experimentalists in narrowing down hypotheses and accelerating protein characterization, we present Protein2Text, a multimodal large language model that interprets protein sequences and generates informative text to address open-ended questions about protein functions and attributes. By integrating a resampling mechanism within an adapted LLaVA framework, our model effectively maps protein sequences into a language-compatible space, enhancing its capability to handle diverse and complex queries. Trained on a newly curated dataset derived from PubMed articles and rigorously evaluated using four comprehensive benchmarks{---}including in-domain and cross-domain evaluations{---}Protein2Text outperforms several existing models in open-ended question-answering tasks. Our work also highlights the limitations of current evaluation metrics applied to template-based approaches, which may lead to misleading results, emphasizing the need for unbiased assessment methods. Our model weights, evaluation datasets, and evaluation scripts are publicly available at https://github.com/alaaj27/Protein2Text.git."
}