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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.
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Asai et al. (Sun,) studied this question.
www.synapsesocial.com/papers/6a08000bc9d6e687e5735c19 — DOI: https://doi.org/10.18653/v1/2023.acl-tutorials.6
Akari Asai
Sewon Min
Zexuan Zhong
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