Large Language Models and Prompt Engineering for Biomedical Query Focused Multi-Document Summarisation
This work addresses biomedical information retrieval and summarization for researchers and practitioners, but it is incremental as it applies known prompt engineering techniques to a specific domain.
The paper tackled biomedical query-focused multi-document summarization using GPT-3.5 with prompt engineering, achieving top ROUGE-F1 results in the 2023 BioASQ Challenge and ranking within the top two runs.
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.