Summary of Beyond Accuracy: Ensuring Correct Predictions with Correct Rationales, by Tang Li et al.
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales
by Tang Li, Mengmeng Ma, Xi Peng
First submitted to arxiv on: 31 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 The proposed two-phase scheme aims to ensure double-correct predictions in foundation models by curating a new dataset with structured rationales for visual recognition tasks and developing a rationale-informed optimization method. The model outperforms state-of-the-art models by up to 10.1% in prediction accuracy across various tasks, while significantly improving rationale correctness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are superhuman at predicting, but what’s behind those predictions? This paper wants to make sure the answers are correct too! To do that, they created a new dataset with reasons why things look certain ways and developed a special way to train models to find those reasons. It works really well – their model is 10% better than others at guessing, and it does a great job of finding and understanding what makes things right or wrong. |
Keywords
* Artificial intelligence * Optimization
