Identifying Non-Replicable Social Science Studies with Language Models

In this study, we investigate whether LLMs can be used to indicate if a study in the behavioural social sciences is replicable. Using a dataset of 14 previously replicated studies (9 successful, 5 unsuccessful), we evaluate the ability of both open-source (Llama 3 8B, Qwen 2 7B, Mistral 7B) and proprietary (GPT-4o) instruction-tuned LLMs to discriminate between replicable and non-replicable findings. We use LLMs to generate synthetic samples of responses from behavioural studies and estimate whether the measured effects support the original findings. When compared with human replication results for these studies, we achieve F1 values of up to $77\%$ with Mistral 7B, $67\%$ with GPT-4o and Llama 3 8B, and $55\%$ with Qwen 2 7B, suggesting their potential for this task. We also analyse how effect size calculations are affected by sampling temperature and find that low variance (due to temperature) leads to biased effect estimates.

@article{saynova2025_2503.10671,
  title={ Identifying Non-Replicable Social Science Studies with Language Models },
  author={ Denitsa Saynova and Kajsa Hansson and Bastiaan Bruinsma and Annika Fredén and Moa Johansson },
  journal={arXiv preprint arXiv:2503.10671},
  year={ 2025 }
}