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        <title>Multilingual Social Media Text Generation and Evaluation with Few-Shot Prompting</title>
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            <title>Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)</title>
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            <namePart type="given">Antoine</namePart>
            <namePart type="family">Bosselut</namePart>
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            <namePart type="given">Khyathi</namePart>
            <namePart type="family">Chandu</namePart>
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            <namePart type="given">Kaustubh</namePart>
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            <namePart type="given">Varun</namePart>
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            <namePart type="given">Jekaterina</namePart>
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            <namePart type="given">Laura</namePart>
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    <abstract>This work adapts large language models to generate multilingual social media text that meets several objectives simultaneously: topic relevance, author style consistency, and reply validity. Leveraging existing online information behavior simulators, which currently only forecast activities but not content, our approach comprised of generalizable prompt formation and efficient evaluation to produce a believable, personalized, and responsive synthetic social network. According to some preliminary experiments, our multi-objective prompt formation and automatic evaluation/selection methods are able to yield a significant number of high-quality synthetic texts according to both standardized and trained metrics.</abstract>
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    <identifier type="doi">10.18653/v1/2022.gem-1.39</identifier>
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%0 Conference Proceedings
%T Multilingual Social Media Text Generation and Evaluation with Few-Shot Prompting
%A Blackburn, Mack
%Y Bosselut, Antoine
%Y Chandu, Khyathi
%Y Dhole, Kaustubh
%Y Gangal, Varun
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Novikova, Jekaterina
%Y Perez-Beltrachini, Laura
%S Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F blackburn-2022-multilingual
%X This work adapts large language models to generate multilingual social media text that meets several objectives simultaneously: topic relevance, author style consistency, and reply validity. Leveraging existing online information behavior simulators, which currently only forecast activities but not content, our approach comprised of generalizable prompt formation and efficient evaluation to produce a believable, personalized, and responsive synthetic social network. According to some preliminary experiments, our multi-objective prompt formation and automatic evaluation/selection methods are able to yield a significant number of high-quality synthetic texts according to both standardized and trained metrics.
%R 10.18653/v1/2022.gem-1.39
%U https://aclanthology.org/2022.gem-1.39
%U https://doi.org/10.18653/v1/2022.gem-1.39
%P 417-427

Markdown (Informal)

[Multilingual Social Media Text Generation and Evaluation with Few-Shot Prompting](https://aclanthology.org/2022.gem-1.39) (Blackburn, GEM 2022)

• Multilingual Social Media Text Generation and Evaluation with Few-Shot Prompting (Blackburn, GEM 2022)

ACL

• Mack Blackburn. 2022. Multilingual Social Media Text Generation and Evaluation with Few-Shot Prompting. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 417–427, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.