PAELLA: Parameter-Efficient Lightweight Language-Agnostic Captioning Model
Rita Ramos, Emanuele Bugliarello, Bruno Martins, Desmond Elliott
Abstract
We introduce PAELLA, a Parameter-Efficient Lightweight Language-Agnostic image captioning model designed to be both parameter and data-efficient using retrieval augmentation. The model is trained by learning a small mapping network with 34M parameters between a pre-trained visual model and a multilingual language model that is conditioned on two types of input: (i) the image itself, and (ii) a set of retrieved captions in the target language. The retrieved examples play a key role in guiding the model to generate captions across languages. Through retrieval, the model can be lightweight in terms of the number of trainable parameters, which only exist in its mapping network, and also in the amount of multilingual training data that is required. Experiments on the XM3600 dataset, featuring 36 languages, show that PAELLA can outperform or compete against some models with 3–77× more learned parameters and 35–863× more data, particularly in low-resource languages. We also find that PAELLA can be trained on only monolingual data and still show strong zero-shot abilities in other languages.- Anthology ID:
- 2024.findings-naacl.225
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3549–3564
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.225
- DOI:
- Cite (ACL):
- Rita Ramos, Emanuele Bugliarello, Bruno Martins, and Desmond Elliott. 2024. PAELLA: Parameter-Efficient Lightweight Language-Agnostic Captioning Model. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3549–3564, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- PAELLA: Parameter-Efficient Lightweight Language-Agnostic Captioning Model (Ramos et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/fix-volume-bibkeys/2024.findings-naacl.225.pdf