Large Language Model (LLM) evaluation is frequently challenged by prompt sensitivity, which limits the reliability of benchmarks relying on a single template. To address this, we systematically investigate a multi-prompt evaluation framework that aggregates performance across diverse templates, leveraging a Quality-Diversity (QD) prompt selection mechanism to ensure both variety and fidelity. Through extensive empirical analysis, we uncover a novel Evaluation Scaling Law: evaluation reliability scales consistently with template diversity. Specifically, our experiments demonstrate that evaluation variance diminishes as the scale of prompts increases, while ranking monotonicity exhibits a log-linear improvement relative to the number of templates. By decoupling a model’s intrinsic capability from template-specific fluctuations, our framework facilitates more reliable assessments of LLMs, thereby providing a solid foundation for benchmarking that enables more effective monitoring of training dynamics and rigorous comparisons among state-of-the-art models.

This paper poses two critical issues in evaluating base models (without post-training): (1) Unstable evaluation during training: in the early stages of pre-training, the models lack the capability to answer questions as required, leading to unstable evaluation results. This instability makes it difficult to provide solid conclusions to guide the training, especially for key experiments such as data ablation and scaling law. (2) Inconsistency between base and instruct models: base models generally exhibit poorer evaluation performance compared to corresponding instruct models. This gap poses a challenge for assessing whether a base model with better evaluation can truly lead to a better instruct model. To address these issues, we propose Base model Oriented Systematic Evaluation (BOSE), a method specifically designed to optimize the evaluation of base models. Specifically, BOSE introduces two key innovations: In-Context Light-instruction Prompt (ICLiP) for open-ended tasks and Blank-ppl for multi-choice tasks with candidate options, which transforms the standard perplexity (ppl) metric into a fill-in-the-blank format to mitigate early-stage evaluation fluctuations. Furthermore, we are the first to propose Kendall’s rank correlation to quantitatively measure the evaluation stability and consistency. Experimental results demonstrate that BOSE significantly enhances both the stability of evaluations during pre-training and the consistency between base and instruct models, thereby providing more reliable guidance for the LLMs’ training.