Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models

arXiv:2405.01686v1 Announce Type: new
Abstract: Meta-analyses statistically aggregate the findings of different randomized controlled trials (RCTs) to assess treatment effectiveness. Because this yields robust estimates of treatment effectiveness, results from meta-analyses are considered the strongest form of evidence. However, rigorous evidence syntheses are time-consuming and labor-intensive, requiring manual extraction of data from individual trials to be synthesized. Ideally, language technologies would permit fully automatic meta-analysis, on demand. This requires accurately extracting numerical results from individual trials, which has been beyond …

abstract arxiv cs.ai cs.cl evidence form however labor language language models large language large language models meta numerical results robust treatment type