Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks

• URL: http://arxiv.org/abs/2404.16966v2
• Date: Wed, 5 Jun 2024 20:14:15 GMT
• Title: Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks
• Authors: Melissa Ailem, Katerina Marazopoulou, Charlotte Siska, James Bono,
• Abstract summary: 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 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, and (3) explanatory factors for these correlations include semantic similarity and common LLM failure points.
• Score: 2.1899189033259305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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