Automated systems are crucial in the healthcare domain due to the vast amount of medical literature. Large Language Models show promise but need exploration in medical Q&A. Different families of language models are compared for suitability in medical applications.
Large language models have advanced generative capabilities, extending beyond traditional tasks like sentiment analysis. The availability of these models to the public has increased, allowing professionals from various backgrounds to access them. Prior work on automated medical question answering is based on information retrieval systems but lacks personalization for patients' specific contexts.
The study aims to fill the gap by comparing general and medical-specific language models for medical Q&A tasks. It evaluates fine-tuning domain-specific models and compares different families of language models. The research addresses critical questions about reliability, comparative performance, and effectiveness in the context of medical Q&A.
Different classes of language models have been developed since the introduction of transformers and attention mechanisms, showing significant progress in generative tasks. The study focuses on decoder-only and encoder-decoder model families to determine the best model for generative question-answering tasks in healthcare.
The research methodology involves testing base LLMs, finetuning distilled versions, and employing prompting techniques for in-context learning. Data augmentation is used to enhance model robustness by training on multiple datasets. Dynamic prompting techniques improve model performance compared to static prompts.
Quantitative results show that dynamic prompting with GPT-3.5 yields better scores than other models on test sets. Data augmentation improves fine-tuned model performance significantly when trained on additional datasets. Qualitative results indicate user preference for answers generated by large GPT models over human-written responses.
Future work includes testing dynamic prompting on newer APIs like GPT-4, fine-tuning larger models like GPT-3 and GPT-4, developing better evaluation metrics, and enhancing datasets through processing and summarization techniques.
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by Niraj Yagnik... klokken arxiv.org 03-07-2024
https://arxiv.org/pdf/2401.11389.pdfDypere Spørsmål