State of What Art? A Call for Multi-Prompt LLM Evaluation

• URL: http://arxiv.org/abs/2401.00595v3
• Date: Mon, 6 May 2024 10:20:26 GMT
• Title: State of What Art? A Call for Multi-Prompt LLM Evaluation
• Authors: Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, Gabriel Stanovsky,
• Abstract summary: We comprehensively analyze the brittleness of results obtained via single-prompt evaluations across 6.5M instances. To improve robustness of the analysis, we propose to evaluate LLMs with a set of diverse prompts instead.
• Score: 28.307860675006545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have led to the development of various evaluation benchmarks. These benchmarks typically rely on a single instruction template for evaluating all LLMs on a specific task. In this paper, we comprehensively analyze the brittleness of results obtained via single-prompt evaluations across 6.5M instances, involving 20 different LLMs and 39 tasks from 3 benchmarks. To improve robustness of the analysis, we propose to evaluate LLMs with a set of diverse prompts instead. We discuss tailored evaluation metrics for specific use cases (e.g., LLM developers vs. developers interested in a specific downstream task), ensuring a more reliable and meaningful assessment of LLM capabilities. We then implement these criteria and conduct evaluations of multiple models, providing insights into the true strengths and limitations of current LLMs.

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