Leveraging Large Language Models to Improve Precision in Randomized Controlled Trials
The study investigated using Large Language Models (LLMs) to safely improve the precision of Randomized Controlled Trials (RCTs). The researchers developed a method to use LLM predictions as covariates, primarily by transforming non-standard data like text into numerical representations. Results showed that this approach can significantly boost precision, especially when standard covariates are lacking or when using text data as input, though the benefit varies by case study.