Abstract
While advances in pre-training have led to dramatic improvements in few-shot learning of NLP tasks, there is limited understanding of what drives successful few-shot adaptation in datasets. In particular, given a new dataset and a pre-trained model, what properties of the dataset make it few-shot learnable, and are these properties independent of the specific adaptation techniques used? We consider an extensive set of recent few-shot learning methods and show that their performance across a large number of datasets is highly correlated, showing that few-shot hardness may be intrinsic to datasets, for a given pre-trained model. To estimate intrinsic few-shot hardness, we then propose a simple and lightweight metric called Spread that captures the intuition that few-shot learning is made possible by exploiting feature-space invariances between training and test samples. Our metric better accounts for few-shot hardness compared to existing notions of hardness and is ~8-100x faster to compute.- Anthology ID:
- 2022.emnlp-main.262
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3955–3963
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.262
- DOI:
- Cite (ACL):
- Xinran Zhao, Shikhar Murty, and Christopher Manning. 2022. On Measuring the Intrinsic Few-Shot Hardness of Datasets. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3955–3963, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- On Measuring the Intrinsic Few-Shot Hardness of Datasets (Zhao et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.262.pdf