Summary of No “zero-shot” Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance, by Vishaal Udandarao et al.
No “Zero-Shot” Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
by Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip H.S. Torr, Adel Bibi, Samuel Albanie, Matthias Bethge
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 The paper investigates how multimodal models’ pretraining datasets affect their performance on downstream tasks. It explores whether these models truly exhibit “zero-shot” generalization or if they require more data for linear improvements. The study uses 34 models and five standard pretraining datasets, generating over 300GB of data artifacts. Results show a log-linear scaling trend, indicating an exponential need for training data to achieve better performance. This challenge is further emphasized by poor model performance on long-tailed test sets. The paper contributes the “Let it Wag!” benchmark for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal models can do amazing things without being trained for specific tasks. But have you ever wondered what makes them so good? The answer might surprise you. This study looked at how well these models perform when given a new task without any training. It turns out that their ability to generalize is not as strong as we thought. In fact, they need much more data to get better and better. This is because the datasets they were trained on didn’t include many of the concepts used in the new tasks. The study also created a new benchmark to help researchers improve these models. |
Keywords
» Artificial intelligence » Generalization » Pretraining » Zero shot
