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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Exploring the effectiveness of instruction tuning in biomedical language processing.

Omid Rohanian¹, Mohammadmahdi Nouriborji², Samaneh Kouchaki³

¹Department of Engineering Science, University of Oxford, Oxford, UK; NLPie Research, Oxford, UK.

Artificial Intelligence in Medicine

|November 14, 2024

Summary

Instruction tuning enhances large language models (LLMs) for biomedical Natural Language Processing (NLP) tasks like Named Entity Recognition. This study demonstrates competitive performance against specialized models using a large, curated instruction dataset.

Keywords:

Biomedical NLP Instruction tuning Llama2-MedTuned Medical NLI Named entity recognition Relation extraction

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Area of Science:

Biomedical Informatics
Natural Language Processing
Artificial Intelligence

Background:

Large Language Models (LLMs) show promise in general language tasks but require domain-specific adaptation for biomedical applications.
Existing LLMs need optimization for specialized downstream tasks like Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natural Language Inference (NLI).

Purpose of the Study:

To investigate the efficacy of instruction tuning for improving LLM performance on biomedical NLP tasks.
To develop and evaluate an instruction-tuned LLM using a large, custom dataset for biomedical applications.

Main Methods:

Applied instruction tuning to two large-scale general LLMs.
Developed a comprehensive instruction-based dataset of approximately 200,000 samples, curated from existing data.
Evaluated the model's performance on classical biomedical NLP tasks.

Main Results:

The instruction-tuned LLM achieved performance comparable to specialized encoder-only models (e.g., BioBERT, BioClinicalBERT) on biomedical NLP tasks.
Analysis provided insights into dataset composition and its impact on model performance.

Conclusions:

Instruction tuning is a viable strategy for enhancing LLM capabilities in specialized biomedical NLP domains.
The developed dataset and models offer a valuable resource for advancing biomedical NLP research.