Summary of Unearthing Skill-level Insights For Understanding Trade-offs Of Foundation Models, by Mazda Moayeri et al.
Unearthing Skill-Level Insights for Understanding Trade-Offs of Foundation Models
by Mazda Moayeri, Vidhisha Balachandran, Varun Chandrasekaran, Safoora Yousefi, Thomas Fel, Soheil Feizi, Besmira Nushi, Neel Joshi, Vibhav Vineet
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper tackles the challenge of evaluating machine learning models’ diverse skills by proposing an automatic approach to parse model-generated rationales. The authors validate their method on 46,000 instances across 12 benchmarks, revealing hundreds of skill-slices – sets of instances testing a common skill. Analyzing these slices provides novel insights into model trade-offs, such as Gemini 1.5 Pro’s strength in computing molar mass but weakness in applying constitutional law compared to GPT-4o and Claude 3.5 Sonnet. The paper demonstrates the practical utility of this approach by showing that insights derived from skill-slice analysis can generalize to held-out instances, leading to a 3% accuracy improvement on the dataset corpus. This work opens up a new avenue in model evaluation, enabling a more granular understanding of model capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a better way to test how well machine learning models can do different tasks. Right now, it’s hard to see which skills a model is good or bad at because we’re only looking at overall scores. The authors created a new method that looks at the reasons why a model makes certain predictions (called rationales) and uses those to figure out what skills are being tested in each instance. They tested this on 46,000 examples across 12 different tasks and found that many models have strengths and weaknesses in specific areas. This can help us make better decisions about which models to use for certain jobs. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt » Machine learning
