2024 NoZeroShotWithoutExponentialDat
- (Udandarao et al., 2024) ⇒ Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma, Philip H. S. Torr, Adel Bibi, Samuel Albanie, and Matthias Bethge. (2024). “No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance.” doi:10.48550/arXiv.2404.04125
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Notes
- The paper demonstrates that claims of Zero-Shot Learning in multimodal models may be overstated, as performance heavily relies on encountering similar concepts during training. It suggests that true zero-shot capabilities might require innovative training strategies that transcend mere exposure to diverse data.
- The paper explores how Multimodal Models integrate information from different modalities but often suffer from inefficiencies due to unbalanced concept exposure in training datasets. It highlights the need for more sophisticated data curation methods to enhance the generalization capabilities of these models.
- The paper identifies significant Sample Inefficiency in current AI models, where linear improvements in performance require exponential increases in data volume. This inefficiency poses challenges for scalable AI development, particularly in data-limited scenarios.
- The paper contributes to the Data-Centric AI field by showing the pivotal role of data quality and distribution in determining the performance of AI models. It calls for a shift towards more rigorous data management practices to improve model robustness and efficiency.
- The paper reveals that Concept Distribution in Datasets significantly impacts model performance, with skewed distributions leading to poor generalization. It advocates for the creation of balanced datasets that more accurately reflect the diversity of real-world scenarios.
- The paper discusses the issue of Image-Text Misalignment in training datasets for multimodal models, where discrepancies between text data and image data can degrade model performance. It underscores the necessity for alignment techniques that ensure consistent and accurate representation across modalities.
- The paper addresses the use of Synthetic Data Utilization in AI Training, illustrating that models can maintain performance trends even when trained on synthetic datasets. This suggests the potential of synthetic data to support the training of robust models without the ethical issues and practical issues associated with real-world data collection.
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Abstract
Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining datasets encompass the downstream concepts targeted for during "zero-shot" evaluation. In this work, we ask: How is the performance of multimodal models on downstream concepts influenced by the frequency of these concepts in their pretraining datasets? We comprehensively investigate this question across 34 models and five standard pretraining datasets (CC-3M, CC-12M, YFCC-15M, LAION-400M, LAION-Aesthetics), generating over 300GB of data artifacts. We consistently find that, far from exhibiting "zero-shot" generalization, multimodal models require exponentially more data to achieve linear improvements in downstream "zero-shot" performance, following a sample inefficient log-linear scaling trend. This trend persists even when controlling for sample-level similarity between pretraining and downstream datasets, and testing on purely synthetic data distributions. Furthermore, upon benchmarking models on long-tailed data sampled based on our analysis, we demonstrate that multimodal models across the board perform poorly. We contribute this long-tail test set as the "Let it Wag!" benchmark to further research in this direction. Taken together, our study reveals an exponential need for training data which implies that the key to "zero-shot" generalization capabilities under large-scale training paradigms remains to be found.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 NoZeroShotWithoutExponentialDat | Vishaal Udandarao Ameya Prabhu Adhiraj Ghosh Yash Sharma Philip H. S. Torr Adel Bibi Samuel Albanie Matthias Bethge | No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance | 10.48550/arXiv.2404.04125 | 2024 |