Automated reproducibility assessments in the social and behavioral sciences using large language models
arXiv:2606.13670v1 Announce Type: new Abstract: Reproducibility in the social and behavioral sciences is typically evaluated by independent researchers who reanalyze the original data to assess whether the published findings can be recovered. However, such approaches are resource-intensive and difficult to scale. Here, we show that large language models (LLMs) can automate reproducibility assessments. Using N=76 published studies with predefined claims from the behavioral and social sciences, we compare LLM-generated analysis with the original findings and human reanalysis. For 7 studies, the
Advances in large language models are reaching a point where they can perform complex analytical tasks, making automation of hitherto resource-intensive processes feasible.
This development indicates a significant step towards automating parts of the research lifecycle, potentially increasing scholarly output efficiency and reproducibility across scientific fields.
The labor-intensive process of reproducibility assessments can now be significantly augmented or potentially replaced by AI, shifting resource allocation in research validation.
- · Social scientists
- · Behavioral scientists
- · AI software developers
- · Academic institutions
- · Human re-analysis researchers
- · Traditional peer review models
LLMs can efficiently conduct reproducibility checks for social and behavioral science studies, saving time and resources.
The widespread adoption of AI-driven reproducibility could accelerate scientific discovery and improve the overall reliability of published research.
This could lead to a re-evaluation of human expert roles in academic validation and potentially redefine standards for research publication and integrity.
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Read at arXiv cs.AI