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        <title>Retrieval-based Language Models and Applications</title>
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        <namePart type="given">Akari</namePart>
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            <title>Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)</title>
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            <namePart type="given">Yun-Nung</namePart>
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            <namePart type="given">Margot</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.acl-tutorials.6</identifier>
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%0 Conference Proceedings
%T Retrieval-based Language Models and Applications
%A Asai, Akari
%A Min, Sewon
%A Zhong, Zexuan
%A Chen, Danqi
%Y Chen, Yun-Nung (Vivian)
%Y Margot, Margot
%Y Reddy, Siva
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F asai-etal-2023-retrieval
%X 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.
%R 10.18653/v1/2023.acl-tutorials.6
%U https://aclanthology.org/2023.acl-tutorials.6
%U https://doi.org/10.18653/v1/2023.acl-tutorials.6
%P 41-46

Markdown (Informal)

[Retrieval-based Language Models and Applications](https://aclanthology.org/2023.acl-tutorials.6) (Asai et al., ACL 2023)

• Retrieval-based Language Models and Applications (Asai et al., ACL 2023)

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.