Abstract
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.
Abstract (translated)
我们为SemEval 2024任务2挑战提供了 baseline，其目标是确定临床研究报告中段落之间的推理关系。我们使用LLM Instruct模型作为语言模型服务（LMaaS）应用提示优化技术。我们观察到，与最近的研究结果一致，人造提示显著增强了手工制作的提示。
URL
https://arxiv.org/abs/2405.01942
