Title: Beyond Benchmarks: Toward Causally Faithful Evaluation of Large Language Models
Primary area: datasets and benchmarks
Keywords: Large language models, Benchmarks, Evaluation methodology, Causal attribution

Abstract: Current large language models (LLMs) evaluations overlook that measured LLM performance is produced on a full evaluation system, including many indispensable components, such as workloads, prompting methods, decoding parameters, and the supporting software–hardware stack.  Without an explicit, controlled specification of the evaluation system, attributing performance differences to the model itself is unreliable.  Our experiments reveal that uncontrolled testing may lead to accuracy variations of up to 70\%. To address this urgent issue, we introduce LLM evaluatology, a principled methodology that reduces the evaluation problem to accurately attributing  the outcomes to the effect of the evaluated LLM, which is a high-dimensional causal-attribution problem. Empirical results demonstrate that LLM evaluatology not only enhances interpretability and causal validity, but also yields evaluations that are more robust, reproducible, and trustworthy than prevailing benchmarks.