State of What Art? A Call for Multi-Prompt LLM Evaluation¶
Arxiv Link - 2024-05-06 10:20:26
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.
Socials¶
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| 🚀 Exciting developments in the field of large language models (LLMs)! A recent study delves into the brittleness of single-prompt evaluations and proposes a more robust approach using diverse prompts. With analysis across 6.5M instances and 20 LLMs, this research offers tailored evaluation metrics for different user cases. Check out the full paper for insights into enhancing the assessment of LLM capabilities: http://arxiv.org/abs/2401.00595v3 #AI #NLP #LLMs #TechResearch 📊🔍 | 🚀 Exciting new research on large language models (LLMs) evaluation benchmarks! This study analyzes the brittleness of single-prompt evaluations across 6.5M instances with 20 LLMs and 39 tasks. Explore how diverse prompts can lead to more robust assessments of LLM capabilities. Check out the full paper here: http://arxiv.org/abs/2401.00595v3 #AI #NLP #LLMs #Research #Tech 🔍📊 |