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BioCreative VI Precision Medicine Track system performance is constrained by entity recognition and variations in corpus characteristics., , , and . Database J. Biol. Databases Curation, (2018)Effects of Human Adversarial and Affable Samples on BERT Generalizability., , , and . CoRR, (2023)Memorization vs. Generalization : Quantifying Data Leakage in NLP Performance Evaluation., , and . EACL, page 1325-1335. Association for Computational Linguistics, (2021)Principles from Clinical Research for NLP Model Generalization., , , and . CoRR, (2023)Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT., , , , and . CoRR, (2022)Effects of Human Adversarial and Affable Samples on BERT Generalizability., , , and . EMNLP (Findings), page 7637-7649. Association for Computational Linguistics, (2023)Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine., , , , , , , , , and 17 other author(s). Database J. Biol. Databases Curation, (2019)Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts., , and . CoRR, (2020)