arXiv:2606.27687v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, classify, and annotate data whose outputs feed downstream hypothesis tests. However, LLM-based research is easy to p-hack: a researcher can tune the prompts, decoding parameters, or output format until a desired result is reached. We propose a protocol to mitigate p-hacking in LLM-based research: preregistering the experiment and eligible models, and then running it on the first eligible LLM that is released after the preregistration. The researcher finalizes the procedure on curre
The rapid development and widespread adoption of LLMs across various research fields highlight the immediate need for robust methodologies to ensure scientific integrity and reproducibility.
This development proposes a critical mechanism to prevent research bias and manipulation in a rapidly evolving area of artificial intelligence, impacting the credibility and reliability of LLM-generated insights.
The explicit proposal of a preregistration protocol for LLM-based research introduces a new standard for scientific rigor, moving towards more transparent and verifiable AI experimentation.
- · Scientific research community
- · Ethical AI developers
- · Researchers using LLMs
- · AI audit and governance platforms
- · Researchers employing p-hacking
- · Unregulated LLM-based research
- · Organizations relying on biased LLM outputs
Increased trust and reliability in research outcomes derived from large language models.
Development of specialized tools and platforms for preregistering and validating LLM experiments.
Potential for regulatory bodies to adopt similar preregistration requirements for AI-driven scientific publications and applications.
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Read at arXiv cs.AI