Instance-level Randomization: Toward More Stable LLM Evaluations (2025)

Abstract: Evaluations of large language models (LLMs) suffer from instability, where small changes of random factors such as few-shot examples can lead to drastic fluctuations of scores and even model rankings. Moreover, different LLMs can have different preferences for a certain setting of random factors. As a result, using a fixed setting of random factors, which is often adopted as the paradigm of current evaluations, can lead to potential unfair comparisons between LLMs. To mitigate the volatility of evaluations, we first theoretically analyze the sources of variance induced by changes in random factors. Targeting these specific sources, we then propose the instance-level randomization (ILR) method to reduce variance and enhance fairness in model comparisons. Instead of using a fixed setting across the whole benchmark in a single experiment, we randomize all factors that affect evaluation scores for every single instance, run multiple experiments and report the averaged score. Theoretical analyses and empirical results demonstrate that ILR can reduce the variance and unfair comparisons caused by random factors, as well as achieve similar robustness level with less than half computational cost compared with previous methods. Codes and data are available at https://github.com/EricLee8/Instance-level-Randomization.

## introduction with large language models (llms) getting stronger @xcite @xcite , evaluation of them is also becoming harder and more important @xcite . one of the biggest challenges of evaluation is that it suffers from instability, where small changes of random factors such as few-shot examples, task descriptions, and even evaluation frameworks can result in significant fluctuations of evaluation scores, * corresponding author. and even model rankings @xcite @xcite ). figure1 illustrates an example where 7 llms are evaluated on hellaswag @xcite dataset. as random factors vary across different runs, model rankings change drastically, with qwen2.5-7b ranking over as large a range as from 1st to 6th. given the above observations, literature can be categorized into the following two groups. one group of works mainly focus on reporting the extreme values or range of fluctuations caused by these random factors. they either utilize automated prompt engineering to find the best or worst prompts, and report the corresponding scores under these prompts @xcite , or design methods to estimate the range of evaluation scores @xcite @xcite . another group of works pay attention on mitigating the instability before the evaluation phase. for example, @xcite adopt constrained decoding to make outputs more consistent, irugalbandara 2024 suggest formatting the multiple-choice tasks as cloze tests to avoid the unstable decoding process. however, few works attempt to enhance the robustness of evaluation @xcite . in this paper, we fill the gap of stabilizing evaluations by proposing an embarrassingly simple yet surprisingly effective method to mitigate the bias and variance of evaluations caused by random factors. in terms of the most adopted paradigm of the current evaluations, it usually adopts a fixed setting of random factors and only runs a single experiment, which imposes biased settings, high variance, and potential unfair comparisons between models due to llms' volatility on the random factors. a natural and naive alleviation is to vary the settings of random factors across multiple runs and report the averaged results @xcite . however, this method suffers a high variance and requires a figure 1 : model ranking variation on hellaswag with different random factors changing, where "f" represents few-shot examples, "o" represents option labels, and "t" represents task descriptions. large number of runs to obtain an unbiased and stable result. based on this, we theoretically analyze the sources of the evaluation instability, based on which we propose our instance-level randomization (ilr) method to stabilize evaluations. specifically, instead of using a fixed setting for a single experiment, we apply random settings to each of the instances in a benchmark. we show both theoretically and empirically that our proposed ilr is able to reduce the variance faster, using less than 50% computational cost to obtain the same variance. in this paper, we study few-shot examples, task descriptions, prompt formats, and option labels as the random factors that affect evaluations, whose detailed explanations are in appendix e. nevertheless, we underline that without the loss of generality, our ilr can be applied to as many as random factors as we can consider. to sum up, the contributions of this work are: instability of evaluations have been discovered and studied by the community for a long time. studies have shown that random factors such as phrasing and wording of prompts @xcite , choice and order of few-shot examples @xcite , as well as their format and structure @xcite , can lead to large variances in model outputs @xcite , even affecting factual accuracy @xcite . given these factors, studies focus on either reporting or mitigating the evaluation instability. ## problems of current evaluation in this section, we show that current evaluation paradigm of llms suffers from biased settings, high variance, and potential unfair comparisons between different models. specifically, there are many random factors that can affect the performance scores of llms in a benchmark, such as instructions, task descriptions, prompt formats, and few-shot examples. however, current benchmarks usually adopt a fixed setting of the above random factors, resulting in biased and unstable evaluations of different llms. ## methodology in this section, we propose a simple yet effective method using instance-level randomization (ilr) over random factors, which can reduce the variance of evaluation scores, and mitigate the instability of model rankings at a lower cost. figure 4 briefly illustrates our ilr method and the comparison between ilr and the normal evaluation setting. the left side illustrates the normal evaluation paradigm, in which the same set of random factor settings is applied to every instance within a benchmark. since a given model tends to exhibit consistent preferences toward the same settings, each instance in the benchmark tends to simultaneously overestimate or underestimate the model's true accuracy for that instance. this leads to unfair, biased, and unstable evaluation results. in contrast, the right side presents our proposed ilr method, which employs different settings for all random factors for each instance. this approach offsets preference effects against each other, resulting in fairer, more stable, and less biased evaluation outcomes. in the subsequent sections, we provide both theoretical and empirical evidence demonstrating that ilr achieves a faster reduction in evaluation variance with fewer runs, thus lowering computational overhead. besides, we also introduce the observed reversal probability (orp) as a novel metric to quantify the stability of model rankings. our experiments confirm that ilr significantly reduces orp, proving its ability to produce fairer, more stable, and ultimately more reliable evaluation results. ## experiments in this section, we first conduct experiments to empirically validate the theoretical analyses in section 4.1 and 4.3. after that, we propose a metric called observational reversal probability (orp) to measure the effectiveness of ilr. the experiments are conducted on winogrande, hellaswag, bigbench-hard @xcite , and mmlu-pro @xcite , with 100 samples on each subset of these datasets if applicable. 4 ) before (marked as fixed) and after (marked as random) instance-level randomization.