Summary of Autobencher: Towards Declarative Benchmark Construction, by Xiang Lisa Li et al.
AutoBencher: Towards Declarative Benchmark Construction
by Xiang Lisa Li, Farzaan Kaiyom, Evan Zheran Liu, Yifan Mai, Percy Liang, Tatsunori Hashimoto
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 AutoBencher is a novel framework for automatically constructing benchmarks that can scalably discover new insights and vulnerabilities of existing language models. Given specific desiderata, such as question difficulty or topic salience, AutoBencher operationalizes each one by casting benchmark creation as an optimization problem. The framework uses a language model to iteratively propose and refine dataset descriptions, which are then used to generate topic-specific questions and answers. We demonstrate the effectiveness of AutoBencher in creating datasets for various domains, including math, multilinguality, knowledge, and safety, with impressive results: 22% more model errors than existing benchmarks. Moreover, AutoBencher helps identify specific gaps not captured by existing benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a special tool that can create new tests to see how well computers understand different topics. This tool is called AutoBencher and it’s like a super-smart helper that figures out what kind of questions would be tricky for computers to answer. By creating these special tests, AutoBencher helps us learn more about what computers are good at and where they need improvement. It also shows us where there might be gaps in their understanding that we didn’t know existed before. This is important because it can help us make better computers that are smarter and more helpful. |
Keywords
* Artificial intelligence * Language model * Optimization
