Large Language Models and Prompt Engineering for Biomedical Query Focused Multi-Document Summarisation

TL;DR

This paper demonstrates that prompt engineering, especially retrieval augmented generation, significantly enhances GPT-3.5's performance in biomedical query-focused multi-document summarisation, achieving top results in the BioASQ Challenge.

Contribution

It shows that carefully designed prompts, including few-shot samples and retrieval augmentation, substantially improve LLM performance in biomedical summarisation tasks.

Findings

Prompt engineering improves summarisation quality.

Retrieval augmented generation yields largest gains.

GPT-3.5 achieves top BioASQ results with proper prompts.

Abstract

This paper reports on the use of prompt engineering and GPT-3.5 for biomedical query-focused multi-document summarisation. Using GPT-3.5 and appropriate prompts, our system achieves top ROUGE-F1 results in the task of obtaining short-paragraph-sized answers to biomedical questions in the 2023 BioASQ Challenge (BioASQ 11b). This paper confirms what has been observed in other domains: 1) Prompts that incorporated few-shot samples generally improved on their counterpart zero-shot variants; 2) The largest improvement was achieved by retrieval augmented generation. The fact that these prompts allow our top runs to rank within the top two runs of BioASQ 11b demonstrate the power of using adequate prompts for Large Language Models in general, and GPT-3.5 in particular, for query-focused summarisation.