# Beyond human gold standards: A multimodel framework for automated abstract classification and information extraction

## TL;DR

This paper introduces a framework using multiple small language models to automate classification and information extraction from scientific abstracts, achieving high accuracy comparable to human experts.

## Contribution

The novel contribution is an agreement-based framework using multiple open-source LLMs to improve reliability and accuracy in automated literature review tasks.

## Key findings

- The framework achieved above 95% accuracy on most abstracts, surpassing human gold standards in 85% of cases.
- Low-performing models contributed fewer accepted decisions, ensuring robust performance despite model variability.
- The approach reduces manual work in systematic reviews while maintaining high accuracy and reproducibility.

## Abstract

Meta-research and evidence synthesis require considerable resources. Large language models (LLMs) have emerged as promising tools to assist in these processes, yet their performance varies across models, limiting their reliability. Taking advantage of the large availability of small size (<10 billion parameters) open-source LLMs, we implemented an agreement-based framework in which a decision is taken only if at least a given number of LLMs produce the same response. The decision is otherwise withheld. This approach was tested on 1020 abstracts of randomized controlled trials in rheumatology, using 2 classic literature review tasks: (1) classifying each intervention as drug or nondrug based on text interpretation and (2) extracting the total number of randomized patients, a task that sometimes required calculations. Re-examining abstracts where at least 4 LLMs disagreed with the human gold standard (dual review with adjudication) allowed constructing an improved gold standard. Compared to a human gold standard and single large LLMs (>70 billion parameters), our framework demonstrated robust performance: several model combinations achieved accuracies above 95% exceeding the human gold standard on at least 85% of abstracts (e.g., 3 of 5 models, 4 of 6 models, or 5 of 7 models). Performance variability across individual models was not an issue, as low-performing models contributed fewer accepted decisions. This agreement-based framework offers a scalable solution that can replace human reviewers for most abstracts, reserving human expertise for more complex cases. Such frameworks could significantly reduce the manual burden in systematic reviews while maintaining high accuracy and reproducibility.