用大模型零样本生成医学科普摘要，效果优于传统方法。
Leveraging Large Language Models for Zero-shot Lay Summarisation in Biomedicine and Beyond
- 设计两阶段框架模拟真实写作流程，提升摘要可读性。
- 大模型生成的摘要在人类评分中更受青睐，且随模型增大表现更好。
- 首次实现NLP论文的零样本通俗化摘要，适合跨领域应用。
本文探索大语言模型在零样本通俗摘要中的应用。提出一种基于真实写作流程的两阶段摘要框架，发现更大模型生成的摘要更受人类评委青睐。评估大模型作为裁判的可靠性，结果表明其能复现人类偏好。初步尝试将该方法应用于自然语言处理领域的论文摘要，验证了模型的泛化能力，并通过深入的人类评估证明所提方法生成的摘要更具实用性。
原文摘要 · Abstract (English)
In this work, we explore the application of Large Language Models to zero-shot Lay Summarisation. We propose a novel two-stage framework for Lay Summarisation based on real-life processes, and find that summaries generated with this method are increasingly preferred by human judges for larger models. To help establish best practices for employing LLMs in zero-shot settings, we also assess the ability of LLMs as judges, finding that they are able to replicate the preferences of human judges. Finally, we take the initial steps towards Lay Summarisation for Natural Language Processing (NLP) articles, finding that LLMs are able to generalise to this new domain, and further highlighting the greater utility of summaries generated by our proposed approach via an in-depth human evaluation.
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