WisPerMed在BioLaySumm上：适配自回归大语言模型用于科学文章的通俗摘要
WisPerMed at BioLaySumm: Adapting Autoregressive Large Language Models for Lay Summarization of Scientific Articles
AI总结：
WisPerMed团队在BioLaySumm2024任务中，通过微调BioMistral和Llama3等大语言模型，结合指令微调、少样本学习与动态专家选择机制，生成生物医学科学文章的通俗摘要，最终取得第4名。
摘要：
本文详细介绍了WisPerMed团队在BioLaySumm2024共享任务中的工作，该任务旨在实现生物医学领域的自动通俗摘要，使非专业人士能够理解科学出版物。团队对大语言模型（LLMs），特别是BioMistral和Llama3模型进行了微调，用于从复杂的科学文本生成通俗摘要。通过多种方法增强了摘要性能，包括指令微调、少样本学习以及为融入特定上下文信息而量身定制的提示变体。实验表明，微调通常在大多数评估指标上表现最佳。少样本学习显著提升了模型生成相关且事实准确文本的能力，尤其是在使用精心设计的提示时。此外，还开发了一种动态专家选择（DES）机制，以基于可读性和事实性指标优化文本输出的选择。在54支参赛队伍中，WisPerMed团队以可读性、事实性和相关性衡量排名第4。根据总分，我们的方法比基线提高了约5.5个百分点，仅落后第1名约1.5个百分点。
英文摘要：
This paper details the efforts of the WisPerMed team in the BioLaySumm2024 Shared Task on automatic lay summarization in the biomedical domain, aimed at making scientific publications accessible to non-specialists. Large language models (LLMs), specifically the BioMistral and Llama3 models, were fine-tuned and employed to create lay summaries from complex scientific texts. The summarization performance was enhanced through various approaches, including instruction tuning, few-shot learning, and prompt variations tailored to incorporate specific context information. The experiments demonstrated that fine-tuning generally led to the best performance across most evaluated metrics. Few-shot learning notably improved the models' ability to generate relevant and factually accurate texts, particularly when using a well-crafted prompt. Additionally, a Dynamic Expert Selection (DES) mechanism to optimize the selection of text outputs based on readability and factuality metrics was developed. Out of 54 participants, the WisPerMed team reached the 4th place, measured by readability, factuality, and relevance. Determined by the overall score, our approach improved upon the baseline by approx. 5.5 percentage points and was only approx 1.5 percentage points behind the first place.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
