The Manchester Bees at PerAnsSumm 2025: Iterative Self-Prompting with Claude and o1 for Perspective-aware Healthcare Answer Summarisation
This system report presents an innovative approach to the PerAnsSumm2025 shared task at the Workshop CL4Health, addressing the critical challenges of perspective-aware healthcare answer summarization. Our method, Iterative Self-Prompting (ISP) with Claude and o1, introduces a novel framework that leverages large language models' ability to iteratively refine their own instructions, achieving competitive results without traditional model training. Despite utilising only API calls rather than computational-intensive training, our system "The Manchester Bees" secured 15th place among 23 leader board systems overall, while demonstrating exceptional performance in key metrics - ranking 6th in Strict-matching-F1 for span identification (Task A) and achieving the highest Factuality score for summary generation (Task B). Notably, our approach achieved state-of-the-art results in specific metrics, including the highest Strict-matching precision (0.2267) for Task A and AlignScore (0.5888) for Task B. This performance, accomplished with minimal computational resources and development time measured in hours rather than weeks, demonstrates the potential of ISP to democratise access to advanced NLP capabilities in healthcare applications. Our complete implementation is available as an open-source project on \url{ https://github.com/pabloRom2004/-PerAnsSumm-2025 }
The Manchester Bees at PerAnsSumm 2025: Iterative Self-Prompting with Claude and o1 for Perspective-aware Healthcare Answer Summarisation. 2025. Pablo Romero, Libo Ren, Lifeng Han, Goran Nenadic. Forthcoming in CL4Health 2025, colocated with NAACL. https://bionlp.nlm.nih.gov/cl4health2025/