Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks
<|reference_start|>Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks: Benchmarks have emerged as the central approach for evaluating Large Language Models (LLMs). The research community often relies on a model's average performance across the test prompts of a benchmark to evaluate the model's performance. This is consistent with the assumption that the test prompts within a benchmark represent a random sample from a real-world distribution of interest. We note that this is generally not the case; instead, we hold that the distribution of interest varies according to the specific use case. We find that (1) the correlation in model performance across test prompts is non-random, (2) accounting for correlations across test prompts can change model rankings on major benchmarks, (3) explanatory factors for these correlations include semantic similarity and common LLM failure points.<|reference_end|>
@article{ailem2024examining,
    title={Examining the robustness of LLM evaluation to the distributional
  assumptions of benchmarks},
    author={Melissa Ailem and Katerina Marazopoulou and Charlotte Siska and James
  Bono},
    journal={arXiv preprint arXiv:2404.16966},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16966},
    primaryClass={cs.CL}
}