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        <title>P5: Plug-and-Play Persona Prompting for Personalized Response Selection</title>
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        <namePart type="given">Joosung</namePart>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose a plug-and-play persona prompting method. Our system can function as a standard open-domain chatbot if persona information is not available. We demonstrate that this approach performs well in the zero-shot setting, which reduces the dependence on persona-ground training data. This makes it easier to expand the system to other languages without the need to build a persona-grounded corpus. Additionally, our model can be fine-tuned for even better performance. In our experiments, the zero-shot model improved the standard model by 7.71 and 1.04 points in the original persona and revised persona, respectively. The fine-tuned model improved the previous state-of-the-art system by 1.95 and 3.39 points in the original persona and revised persona, respectively. To the best of our knowledge, this is the first attempt to solve the problem of personalized response selection using prompt sequences. Our code is available on github.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.1031</identifier>
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%0 Conference Proceedings
%T P5: Plug-and-Play Persona Prompting for Personalized Response Selection
%A Lee, Joosung
%A Oh, Minsik
%A Lee, Donghun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F lee-etal-2023-p5
%X The use of persona-grounded retrieval-based chatbots is crucial for personalized conversations, but there are several challenges that need to be addressed. 1) In general, collecting persona-grounded corpus is very expensive. 2) The chatbot system does not always respond in consideration of persona at real applications. To address these challenges, we propose a plug-and-play persona prompting method. Our system can function as a standard open-domain chatbot if persona information is not available. We demonstrate that this approach performs well in the zero-shot setting, which reduces the dependence on persona-ground training data. This makes it easier to expand the system to other languages without the need to build a persona-grounded corpus. Additionally, our model can be fine-tuned for even better performance. In our experiments, the zero-shot model improved the standard model by 7.71 and 1.04 points in the original persona and revised persona, respectively. The fine-tuned model improved the previous state-of-the-art system by 1.95 and 3.39 points in the original persona and revised persona, respectively. To the best of our knowledge, this is the first attempt to solve the problem of personalized response selection using prompt sequences. Our code is available on github.
%R 10.18653/v1/2023.emnlp-main.1031
%U https://aclanthology.org/2023.emnlp-main.1031
%U https://doi.org/10.18653/v1/2023.emnlp-main.1031
%P 16571-16582

Markdown (Informal)

[P5: Plug-and-Play Persona Prompting for Personalized Response Selection](https://aclanthology.org/2023.emnlp-main.1031) (Lee et al., EMNLP 2023)

• P5: Plug-and-Play Persona Prompting for Personalized Response Selection (Lee et al., EMNLP 2023)

ACL

• Joosung Lee, Minsik Oh, and Donghun Lee. 2023. P5: Plug-and-Play Persona Prompting for Personalized Response Selection. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16571–16582, Singapore. Association for Computational Linguistics.