arXiv:2405.14782v3 Announce Type: replace Abstract: Reliable evaluation of language models (LMs) remains an open challenge. Re- searchers and engineers face methodological issues such as the sensitivity of models to evaluation setup, difficulty of proper comparisons across methods, and the lack of reproducibility and transparency. Evaluation difficulties are exacer- bated by the fracturing and siloing of information about conventions and common practices. In this paper we draw on three years of experience in evaluating large lan- guage models (LMs) as developers of the popular Language Model E
The rapid advancement and widespread deployment of large language models have exposed significant challenges in reliable and reproducible evaluation, making this a critical juncture for establishing best practices.
Reliable evaluation is fundamental for the progress and trustworthiness of AI, directly impacting research, development, and deployment of effective language models across various sectors.
Increased focus on standardized and transparent evaluation methodologies will lead to more robust and comparable AI research outcomes, fostering better-designed and more accurately assessed language models.
- · AI researchers
- · AI developers
- · AI ethics and safety organizations
- · Organizations relying on opaque evaluation methods
- · Unscientific AI research practices
Improved reproducibility in language model evaluations will accelerate research and development cycles.
More reliable benchmarks will differentiate truly performant models, potentially shifting market leadership and investment towards scientifically validated approaches.
Enhanced trust in AI system performance could lead to broader and faster adoption of AI in critical applications, influencing economic and societal structures.
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