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.
arXiv:2507. 04491v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly being integrated into psychological and behavioral research as research tools, evaluation targets, human simulators, and cognitive models.
arXiv:2604. 02458v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate human responses and estimate treatment effect of interventions when real-world experiments are costly or infeasible.
arXiv:2606. 07936v1 Announce Type: cross Abstract: Human evaluation plays a critical role in assessing the quality of generated text.
SciLitBench is a multi-stage benchmark for evaluating large language models (LLMs) in systematic literature reviews, covering title and abstract screening, full-text screening, and schema-guided data extraction across 42,981 records and 888 included papers. The study shows that explicit inclusion/exclusion criteria boost title and abstract screening performance by 28.8% and researcher-authored rationales improve full-text screening by 15%. Data extraction performance varies widely, with high accuracy for publication year but low overlap for computational approaches, and even the best models recover only a fraction of annotated evidence and limitations.
arXiv:2606. 17165v1 Announce Type: cross Abstract: Organizations and researchers show increasing interest in using large language models (LLMs) in place of human participants in A/B tests, in the hope of experimenting faster and at lower cost.
arXiv:2605. 11954v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in social science as scalable measurement tools for converting unstructured text into variables that can enter standard empirical designs.
The paper examines whether removing declared language fields from de‑identified résumés eliminates demographic leakage in large language models. By keeping language attributes identical and varying only unstructured prose across five ethnocultural groups and three cue‑salience levels, the authors find that non‑language text still allows target‑group recovery (average 0.757, reaching 1.000 under high salience). They also show that evaluation design—such as allowing or forbidding ties—dramatically affects LLM‑as‑a‑judge outcomes, underscoring the importance of evaluation protocol in bias audits.
arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.
arXiv:2608.28626v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This st...
arXiv:2506. 16697v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering psychological research both as tools and as objects of inquiry.
arXiv:2609.24516v1 Announce Type: new Abstract: In recent years, large language models (LLMs) have emerged as a popular alternative for evaluation. Often referred to as LLMs as judges (LLJs), these s...
The paper "Medical Causal Hypothesis Verification with Large Language Models" reports a small-scale study evaluating eight LLMs on 17 medical causal hypotheses. The authors introduce an evaluation framework and annotate 1,067 evidence points across six criteria, using nine metrics to assess performance. Results show that while LLMs have strong recall, they frequently fail to provide valid scientific articles, evidence, or reject unsupported hypotheses, revealing a critical limitation for their use in healthcare.