Title: Insights from the Frontlines: Ensuring Reproducible Evaluation of Language Models

Abstract:

The quest for dependable evaluation of language models (LMs) continues to present significant hurdles. Practitioners and researchers alike encounter methodological obstacles, including model sensitivity to evaluation configurations, complexities in conducting fair comparisons between different approaches, and persistent deficits in transparency and reproducibility. These issues are compounded by the fragmentation and isolation of knowledge regarding established conventions and standard practices.

Drawing upon three years of hands-on experience evaluating large language models (LMs) as the creators of the widely used Language Model Evaluation Harness (lm-eval) framework (Gao et al., 2023), this paper offers strategic guidance and key lessons for the community. We outline various challenges encountered by practitioners and provide specific examples illustrating how these difficulties—or the lack of established best practices—have impacted real-world scenarios. Our aim is to propose recommendations that enhance the rigor and reliability of LM evaluations, while simultaneously formalizing much of the informal or "folk" knowledge surrounding the field to establish a robust foundation for future advancements.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC