# When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications

arXiv: https://arxiv.org/abs/2005.11700 · HTML: https://mlsanity.com/paper/2005.11700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 5 of 5)

- [An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning](https://mlsanity.com/paper/2308.00788/) (2023)
- [A Survey of Data-Efficient Graph Learning](https://mlsanity.com/paper/2402.00447/) (2024)
- [Sign-MAML: Efficient Model-Agnostic Meta-Learning by SignSGD](https://mlsanity.com/paper/2109.07497/) (2021)
- [Meta-Learning with a Geometry-Adaptive Preconditioner](https://mlsanity.com/paper/2304.01552/) (2023)
- [Meta-learning to Address Data Shift in Time Series Classification](https://mlsanity.com/paper/2601.09018/) (2026)

## Abstract (arXiv)

Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation. Many impacting factors, including data quantity, similarity among tasks, and the balance between general language model and task-specific adaptation, can affect the performance of MAML in NLP, but few works have thoroughly studied them. In this paper, we conduct an empirical study to investigate these impacting factors and conclude when MAML works the best based on the experimental results.