Biobigbird model processes long biomedical texts for better understanding
BioBigBird: A Sparse Attention Model for Long-Range Dependency Processing in Biomedical Text
Computation and LanguageMachine Learning
Summary
Understanding long, complex biomedical texts is hard for most language models because they can only read short chunks at a time. The authors created BioBigBird, a model that can read much longer texts by using a special technique called sparse attention. This helps it find connections across big pieces of text, like medical papers. It was trained to recognize important medical terms and how they relate to each other, which improved its performance on tests. This makes BioBigBird useful for understanding intricate biomedical information better than some earlier models.
What this means in practice
- •For clinical data teams: Improve extraction of detailed medical information from long clinical reports using a model that handles extended text sequences.
- •For biomedical text miners: Extract complex relationships and entities from full-length biomedical papers to enhance knowledge discovery workflows.
Authors
Roshan Balaji, Pavan Kumar S, Vasudev Gupta, Sreejith N, Keerthana Sridhar, Nirav Bhatt
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 https://huggingface.co/collections/bisectgroup/biobigbird.