We present a baseline for the SemEval 2024 task 2 challenge, whose objective is to ascertain the inference relationship between pairs of clinical trial report sections and statements. We apply prompt optimization techniques with LLM Instruct models provided as a Language Model-as-a-Service (LMaaS). We observed, in line with recent findings, that synthetic CoT prompts significantly enhance manually crafted ones.

翻译：我们提出了SemEval 2024任务2挑战赛的一个基线方案，该任务旨在确定临床试验报告章节与陈述对之间的推理关系。我们采用以语言模型即服务（LMaaS）形式提供的LLM Instruct模型，应用了提示优化技术。根据近期研究进展，我们观察到合成思维链（CoT）提示能够显著增强人工设计的提示效果。