BioBigBird: A Sparse Attention Model for Long-Range Dependency Processing in Biomedical Text

Abstract:While domain-specific Large Language Models (LLMs) have encoded vast biomedical knowledge, their limited context windows often hinder a deep understanding of nuanced relationships within and across texts. To address this limitation, we introduce BioBigBird, a bidirectional language model pre-trained on extensive biomedical literature and clinical data, specifically designed to handle long-range dependencies. BioBigBird leverages a sparse attention mechanism to process sequences up to 4096 tokens, and its training incorporates a multi-stage process to mitigate noise from the large-scale pre-training corpus. We further enhance its performance by employing a multi-task learning (MTL) framework that jointly optimizes for Named Entity Recognition and Relation Extraction. Comprehensive evaluations on the BLURB benchmark reveal that our MTL-enhanced BioBigBird achieves highly competitive results against state-of-the-art models. Our work contributes an effective methodology for developing powerful, long-context language models for specialized domains, demonstrating the value of extended sequence processing for complex text analysis. Our models are publicly available at this https URL.

Computation and Language (cs.CL); Machine Learning (cs.LG)

arXiv:2610.11430 [cs.CL]

(or arXiv:2610.11430v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.11430 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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