Abstract
Retrieval-based language models (LMs) have shown impressive performance on diverse NLP tasks. In this tutorial, we will provide a comprehensive and coherent overview of recent advances in retrieval-based LMs. We will start by providing preliminaries covering the foundation of LMs (e.g., masked LMs, autoregressive LMs) and retrieval systems (e.g., nearest-neighbor search). We will then detail recent progress in retrieval-based models, focusing on their model architectures and learning approaches. Finally, we will show how retrieval-based LMs are adapted to downstream applications, and extended to multilingual and multi-modal settings. Finally, we will use an exercise to showcase the effectiveness of retrieval-based LMs.- Anthology ID:
- 2023.acl-tutorials.6
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Yun-Nung (Vivian) Chen, Margot Margot, Siva Reddy
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 41–46
- Language:
- URL:
- https://aclanthology.org/2023.acl-tutorials.6
- DOI:
- 10.18653/v1/2023.acl-tutorials.6
- Cite (ACL):
- Akari Asai, Sewon Min, Zexuan Zhong, and Danqi Chen. 2023. Retrieval-based Language Models and Applications. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts), pages 41–46, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Retrieval-based Language Models and Applications (Asai et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.acl-tutorials.6.pdf