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        <title>Methods, Applications, and Directions of Learning-to-Rank in NLP Research</title>
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        <namePart type="given">Justin</namePart>
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    <name type="personal">
        <namePart type="given">Tegan</namePart>
        <namePart type="family">Maharaj</namePart>
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    <name type="personal">
        <namePart type="given">Sowmya</namePart>
        <namePart type="family">Vajjala</namePart>
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            <title>Findings of the Association for Computational Linguistics: NAACL 2024</title>
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            <namePart type="given">Kevin</namePart>
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            <namePart type="given">Helena</namePart>
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            <namePart type="given">Steven</namePart>
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    <abstract>Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web search and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.</abstract>
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%0 Conference Proceedings
%T Methods, Applications, and Directions of Learning-to-Rank in NLP Research
%A Lee, Justin
%A Bernier-Colborne, Gabriel
%A Maharaj, Tegan
%A Vajjala, Sowmya
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lee-etal-2024-methods
%X Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web search and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.
%R 10.18653/v1/2024.findings-naacl.123
%U https://aclanthology.org/2024.findings-naacl.123
%U https://doi.org/10.18653/v1/2024.findings-naacl.123
%P 1900-1917

Markdown (Informal)

[Methods, Applications, and Directions of Learning-to-Rank in NLP Research](https://aclanthology.org/2024.findings-naacl.123) (Lee et al., Findings 2024)

• Methods, Applications, and Directions of Learning-to-Rank in NLP Research (Lee et al., Findings 2024)

ACL

• Justin Lee, Gabriel Bernier-Colborne, Tegan Maharaj, and Sowmya Vajjala. 2024. Methods, Applications, and Directions of Learning-to-Rank in NLP Research. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1900–1917, Mexico City, Mexico. Association for Computational Linguistics.