Summary of More Than Marketing? on the Information Value Of Ai Benchmarks For Practitioners, by Amelia Hardy et al.
More than Marketing? On the Information Value of AI Benchmarks for Practitioners
by Amelia Hardy, Anka Reuel, Kiana Jafari Meimandi, Lisa Soder, Allie Griffith, Dylan M. Asmar, Sanmi Koyejo, Michael S. Bernstein, Mykel J. Kochenderfer
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 investigates the role of public AI benchmark results in informing decision-making processes. Interviews with 19 individuals who have used or decided against using benchmarks reveal that they primarily serve as a signal of relative performance difference between models, rather than a definitive indicator of model superiority. The study finds that academic settings view public benchmarks as suitable measures for capturing research progress, whereas product and policy applications often find them inadequate for informing substantive decisions. To create effective benchmarks, the authors suggest providing meaningful, real-world evaluations, incorporating domain expertise, maintaining transparency in scope and goals, and accounting for trade-offs in model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks into how AI benchmark results are used to make decisions. Researchers talked to 19 people who use or don’t use benchmarks in their daily work. The findings show that these scores mainly tell you which models do better compared to others, not which one is the best overall. In schools, these public scores are useful for tracking progress, but businesses and policymakers often find them not helpful enough. To make good benchmark tests, experts recommend making them relevant to real-life situations, including expert opinions, being clear about what they’re testing, and showing how different models perform in different ways. |
Keywords
» Artificial intelligence » Tracking
