[Paper] Automated reproducibility assessments in the social and behavioral sciences using large language models
Source: arXiv - 2606.13670v1
Overview
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 LLM could not produce a viable effect size estimate. For the remaining studies, our LLM pipeline recovered the original effect sizes in 41% of studies using a +/-0.05 tolerance in Cohen’s d. Further, our LLM pipeline reached the same qualitative conclusion as the original study in 96% of cases, where conclusions indicate whether the reanalysis supports the original claim. For comparison, human reanalysts recovered the original effect sizes in 34% of studies and reached the same qualitative conclusion in 74% of cases. Together, these results show that LLMs can serve as a scalable tool for automated reproducibility assessment and provide a foundation for systematic auditing of empirical results in the social and behavioral sciences.
Key Contributions
This paper presents research in the following areas:
- cs.AI
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.AI.
Authors
- Tobias Holtdirk
- Pietro Marcolongo
- Anna Steinberg Schulten
- Felix Henninger
- Stefan Rose
- Sarah Ball
- Bolei Ma
- Frauke Kreuter
- Markus Weinmann
- Stefan Feuerriegel
Paper Information
- arXiv ID: 2606.13670v1
- Categories: cs.AI
- Published: June 11, 2026
- PDF: Download PDF