arXiv:2601.21816v2 Announce Type: replace Abstract: Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards. However, many existing approaches either rely on restrictive parametric assumptions or lack valid uncertainty quantification when flexible machine learning methods are used. In this paper, we propose a nonparametric statistical framework, called DMLRank, for comparing and ranking LLMs from preference data using debiased machine learning (DML). For this, we introduce generalized average ranking scores (GARS), which g
The proliferation of advanced LLMs and the increasing economic stakes associated with their performance necessitate more robust, nonparametric evaluation methods.
Reliable, unbiased evaluation of LLMs is critical for fostering innovation, guiding investment, and establishing transparent leaderboards, impacting enterprise adoption and development.
Current, often subjective or parametrically-biased LLM evaluation methods can be replaced or augmented by a statistically rigorous framework capable of valid uncertainty quantification.
- · LLM researchers
- · Developers of open-source LLMs
- · Enterprises adopting LLMs
- · AI fairness and ethics organizations
- · LLM developers relying on opaque evaluation methods
- · Benchmarking organizations with less rigorous statistical approaches
More accurate and trustworthy LLM leaderboards will emerge, influencing market perception and investment.
This improved evaluation could accelerate the development of more performant and robust LLMs by providing clearer feedback loops.
Standardization of such nonparametric evaluation techniques could become a regulatory or industry expectation for AI product claims.
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Read at arXiv cs.LG