From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

• URL: http://arxiv.org/abs/2406.11939v2
• Date: Mon, 14 Oct 2024 18:11:58 GMT
• Title: From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
• Authors: Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica,
• Abstract summary: BenchBuilder is an automated pipeline that curates high-quality, open-ended prompts from large, crowd-sourced datasets. We release Arena-Hard-Auto, a benchmark consisting 500 challenging prompts curated by BenchBuilder. Our work sets a new framework for the scalable curation of automated benchmarks from extensive data.
• Score: 47.19203597218352
• License:
• Abstract: The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks. However, manual curation of high-quality, human-aligned benchmarks is expensive and time-consuming. To address this, we introduce BenchBuilder, an automated pipeline that leverages LLMs to curate high-quality, open-ended prompts from large, crowd-sourced datasets, enabling continuous benchmark updates without human in the loop. We apply BenchBuilder to datasets such as Chatbot Arena and WildChat-1M, extracting challenging prompts and utilizing LLM-as-a-Judge for automatic model evaluation. To validate benchmark quality, we propose new metrics to measure a benchmark's alignment with human preferences and ability to separate models. We release Arena-Hard-Auto, a benchmark consisting 500 challenging prompts curated by BenchBuilder. Arena-Hard-Auto provides 3x higher separation of model performances compared to MT-Bench and achieves 98.6% correlation with human preference rankings, all at a cost of $20. Our work sets a new framework for the scalable curation of automated benchmarks from extensive data.

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