Test Data

Test data is crucial for evaluating the performance and robustness of machine learning models, particularly large language models (LLMs), but its creation and utilization present significant challenges. Current research focuses on improving the efficiency of test data selection through active testing methods and leveraging knowledge graphs and LLMs to automate data extraction and validation, particularly in complex domains like aerospace. Addressing issues like data contamination, bias from overlapping training and test sets, and the need for analysis-naive holdout data are key concerns impacting the reliability and generalizability of model evaluations across various applications. These efforts aim to enhance the rigor and reproducibility of machine learning research and improve the reliability of deployed models.