@inproceedings{yong-etal-2023-prompting,
title = "Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South {E}ast {A}sian Languages",
author = "Yong, Zheng Xin and
Zhang, Ruochen and
Forde, Jessica and
Wang, Skyler and
Subramonian, Arjun and
Lovenia, Holy and
Cahyawijaya, Samuel and
Winata, Genta and
Sutawika, Lintang and
Cruz, Jan Christian Blaise and
Tan, Yin Lin and
Phan, Long and
Phan, Long and
Garcia, Rowena and
Solorio, Thamar and
Aji, Alham",
editor = "Winata, Genta and
Kar, Sudipta and
Zhukova, Marina and
Solorio, Thamar and
Diab, Mona and
Sitaram, Sunayana and
Choudhury, Monojit and
Bali, Kalika",
booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.calcs-1.5",
pages = "43--63",
abstract = "The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.",
}
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<abstract>The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.</abstract>
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%0 Conference Proceedings
%T Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages
%A Yong, Zheng Xin
%A Zhang, Ruochen
%A Forde, Jessica
%A Wang, Skyler
%A Subramonian, Arjun
%A Lovenia, Holy
%A Cahyawijaya, Samuel
%A Winata, Genta
%A Sutawika, Lintang
%A Cruz, Jan Christian Blaise
%A Tan, Yin Lin
%A Phan, Long
%A Garcia, Rowena
%A Solorio, Thamar
%A Aji, Alham
%Y Winata, Genta
%Y Kar, Sudipta
%Y Zhukova, Marina
%Y Solorio, Thamar
%Y Diab, Mona
%Y Sitaram, Sunayana
%Y Choudhury, Monojit
%Y Bali, Kalika
%S Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yong-etal-2023-prompting
%X The differences in decision making between behavioural models of voice interfaces are hard to capture using existing measures for the absolute performance of such models. For instance, two models may have a similar task success rate, but very different ways of getting there. In this paper, we propose a general methodology to compute the similarity of two dialogue behaviour models and investigate different ways of computing scores on both the semantic and the textual level. Complementing absolute measures of performance, we test our scores on three different tasks and show the practical usability of the measures.
%U https://aclanthology.org/2023.calcs-1.5
%P 43-63
Markdown (Informal)
[Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages](https://aclanthology.org/2023.calcs-1.5) (Yong et al., CALCS 2023)
ACL
- Zheng Xin Yong, Ruochen Zhang, Jessica Forde, Skyler Wang, Arjun Subramonian, Holy Lovenia, Samuel Cahyawijaya, Genta Winata, Lintang Sutawika, Jan Christian Blaise Cruz, Yin Lin Tan, Long Phan, Long Phan, Rowena Garcia, Thamar Solorio, and Alham Aji. 2023. Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages. In Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching, pages 43–63, Singapore. Association for Computational Linguistics.