Exploring the Effectiveness of Instruction Tuning in Biomedical Language
  Processing
Omid Rohanian Mohammadmahdi Nouriborji David A. Clifton
Large Language Models (LLMs), particularly those similar to ChatGPT, have
significantly influenced the field of Natural Language Processing (NLP). While
these models excel in general language tasks, their performance in
domain-specific downstream tasks such as biomedical and clinical Named Entity
Recognition (NER), Relation Extraction (RE), and Medical Natural Language
Inference (NLI) is still evolving. In this context, our study investigates the
potential of instruction tuning for biomedical language processing, applying
this technique to two general LLMs of substantial scale. We present a
comprehensive, instruction-based model trained on a dataset that consists of
approximately $200,000$ instruction-focused samples. This dataset represents a
carefully curated compilation of existing data, meticulously adapted and
reformatted to align with the specific requirements of our instruction-based
tasks. This initiative represents an important step in utilising such models to
achieve results on par with specialised encoder-only models like BioBERT and
BioClinicalBERT for various classical biomedical NLP tasks. Our work includes
an analysis of the dataset's composition and its impact on model performance,
providing insights into the intricacies of instruction tuning. By sharing our
codes, models, and the distinctively assembled instruction-based dataset, we
seek to encourage ongoing research and development in this area.