Team IELAB at TREC Clinical Trial Track 2023：利用神经排序器和大型语言模型增强临床试验检索
Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models
AI总结：
ielab团队利用ChatGPT生成合成患者描述以扩充训练数据，训练基于PubmedBERT的稠密与稀疏检索器及交叉编码器重排序器，并使用GPT-4作为标注器进行结果重排序，提出了一种结合大型语言模型与神经排序器的临床试验检索新方法。
摘要：
我们描述了来自CSIRO和昆士兰大学的ielab团队参加2023 TREC Clinical Trials Track的方法。我们的方法是使用神经排序器，但利用大型语言模型来克服此类排序器缺乏训练数据的问题。具体而言，我们使用ChatGPT为语料库中随机选择的临床试验生成相关的患者描述。该合成数据集与往年的人工标注训练数据相结合，用于训练基于PubmedBERT的稠密和稀疏检索器。此外，系统还集成了一个交叉编码器重排序器。为进一步提升方法的有效性，我们提示GPT-4作为TREC标注器对我们的运行文件提供判断。这些判断随后被用于对结果进行重排序。该架构在SOTA大型语言模型的辅助下紧密集成了强大的基于PubmedBERT的排序器，展示了一种临床试验检索的新方法。
英文摘要：
We describe team ielab from CSIRO and The University of Queensland's approach to the 2023 TREC Clinical Trials Track. Our approach was to use neural rankers but to utilise Large Language Models to overcome the issue of lack of training data for such rankers. Specifically, we employ ChatGPT to generate relevant patient descriptions for randomly selected clinical trials from the corpus. This synthetic dataset, combined with human-annotated training data from previous years, is used to train both dense and sparse retrievers based on PubmedBERT. Additionally, a cross-encoder re-ranker is integrated into the system. To further enhance the effectiveness of our approach, we prompting GPT-4 as a TREC annotator to provide judgments on our run files. These judgments are subsequently employed to re-rank the results. This architecture tightly integrates strong PubmedBERT-based rankers with the aid of SOTA Large Language Models, demonstrating a new approach to clinical trial retrieval.
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。
