Why LLM Evaluation Results Aren't Reproducible (And What to Do About It)

Reproducibility in large language model (LLM) evaluations is a significant challenge due to factors such as model updates, probabilistic outputs, and hardware variability. Commercial LLM providers frequently update their models, leading to discrepancies in results over time, while the inherent probabilistic nature of LLMs means that the same prompt can yield different outputs on different runs. Additionally, variations in hardware configurations can cause subtle differences in calculations, further complicating consistency. A lack of detailed documentation in research papers, such as prompt specifics and system settings, exacerbates these issues. Solutions include explicitly pinning model versions, using standardized evaluation frameworks, and meticulously documenting every aspect of experiments. Community-driven initiatives are working toward standardized reporting to enhance trust and reproducibility, emphasizing the importance of repeatable experiments for verifiable and reliable results.