Optimized Biomedical Question-Answering Services with LLM and Multi-BERT Integration

TL;DR

This paper introduces an integrated biomedical question-answering system combining LLMs with Multi-BERT models, enhancing data processing, response accuracy, and adaptability to support healthcare professionals with complex information retrieval.

Contribution

It presents a novel integration of LLMs with Multi-BERT configurations, including a training strategy to prevent overfitting and improve response efficiency in biomedical QA tasks.

Findings

Improved response accuracy on BioASQ and BioMRC datasets.

Enhanced adaptability of QA system through BERT freezing technique.

Demonstrated potential to support healthcare decision-making.

Abstract

We present a refined approach to biomedical question-answering (QA) services by integrating large language models (LLMs) with Multi-BERT configurations. By enhancing the ability to process and prioritize vast amounts of complex biomedical data, this system aims to support healthcare professionals in delivering better patient outcomes and informed decision-making. Through innovative use of BERT and BioBERT models, combined with a multi-layer perceptron (MLP) layer, we enable more specialized and efficient responses to the growing demands of the healthcare sector. Our approach not only addresses the challenge of overfitting by freezing one BERT model while training another but also improves the overall adaptability of QA services. The use of extensive datasets, such as BioASQ and BioMRC, demonstrates the system's ability to synthesize critical information. This work highlights how advanced language models can make a tangible difference in healthcare, providing reliable and responsive tools for professionals to manage complex information, ultimately serving the broader goal of improved care and data-driven insights.