Summary of Automatically Extracting Numerical Results From Randomized Controlled Trials with Large Language Models, by Hye Sun Yun et al.
Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models
by Hye Sun Yun, David Pogrebitskiy, Iain J. Marshall, Byron C. Wallace
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper discusses the potential of large language models (LLMs) in performing automatic meta-analyses of randomized controlled trials (RCTs). Meta-analyses are a powerful tool for assessing treatment effectiveness, but they require labor-intensive manual extraction of data from individual trials. The authors aim to evaluate whether modern LLMs can accurately extract numerical results from trial reports, which would enable fully automatic meta-analysis on demand. They create an evaluation dataset of clinical trial reports with numerical findings and test the performance of seven LLMs applied zero-shot for this task. The results show that massive LLMs are close to achieving fully automatic meta-analysis for binary outcomes but struggle with complex outcome measures requiring inference. This work charts a path toward fully automatic meta-analysis of RCTs via LLMs, highlighting both the potential and limitations of existing models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models could help analyze many medical studies at once, making it easier to understand which treatments are most effective. Researchers created a test dataset with information from several medical trials and used seven different large language models to see how well they could extract important numbers from these reports. The results show that some of the models can do a good job of extracting simple information, but struggle when the information is more complex. This research helps us understand what’s possible and what’s not with using large language models for medical studies. |
Keywords
» Artificial intelligence » Inference » Zero shot
