Summary of From Crowdsourced Data to High-quality Benchmarks: Arena-hard and Benchbuilder Pipeline, by Tianle Li et al.
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
by Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Tianhao Wu, Banghua Zhu, Joseph E. Gonzalez, Ion Stoica
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces BenchBuilder, an automated pipeline that uses Large Language Models (LLMs) to curate high-quality, open-ended prompts from large datasets, enabling continuous benchmark updates without human intervention. The authors apply this pipeline to datasets such as Chatbot Arena and WildChat-1M, extracting challenging prompts and utilizing LLMs for automatic model evaluation. To validate the quality of these benchmarks, the authors propose new metrics that measure a benchmark’s alignment with human preferences and ability to separate models. They release Arena-Hard-Auto, a benchmark consisting 500 challenging prompts curated by BenchBuilder, which 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BenchBuilder is a new way to create tests for artificial intelligence models called Large Language Models (LLMs). Right now, testing these models is hard because we need lots of high-quality test questions that are difficult but not impossible for humans or computers to answer. BenchBuilder uses LLMs to make these test questions automatically from big datasets like Chatbot Arena and WildChat-1M. This makes it possible to create new tests all the time without needing human help. The authors also came up with new ways to measure how good these tests are at showing which models are better than others. |
Keywords
* Artificial intelligence * Alignment
