arXiv:2607.07915v1 Announce Type: cross Abstract: Large language models (LLMs) are reshaping social science methodology. Researchers increasingly prompt language models to generate quantitative measurements of social concepts, for example labeling data or simulating survey responses. Yet LLMs pose methodological challenges including bias, hallucination, and brittleness across contexts, with unclear threats to validity. Standard practices and norms for addressing these challenges are still emerging. We collect and systematically analyze validation practices in a comprehensive corpus of papers f
The rapid proliferation and adoption of LLMs in academic research, particularly social sciences, necessitates immediate attention to their methodological implications and potential biases, making this validation research timely.
A strategic reader needs to understand the inherent biases and threats to validity in LLM-generated data, as this impacts the reliability of research, policy recommendations, and public discourse informed by such models.
The push for systematic validation practices establishes new norms for responsible AI integration in research, potentially standardizing how LLMs are used and evaluated in academic and professional settings.
- · AI ethics researchers
- · Social scientists with robust validation methodologies
- · Responsible AI developers
- · Academic publishers
- · Researchers using LLMs uncritically
- · AI models with unaddressed biases
- · Academic fields reliant on poorly validated LLM outputs
Increased scrutiny and demand for transparency in research utilizing large language models.
Development of new academic tools and frameworks specifically designed to validate LLM outputs across various disciplines.
Potential for a 'reproducibility crisis' in social science if validation practices are not widely adopted, eroding public trust in LLM-informed research.
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