@inproceedings{mok-etal-2024-llm,
title = "{LLM}-based Frameworks for {API} Argument Filling in Task-Oriented Conversational Systems",
author = "Mok, Jisoo and
Kachuee, Mohammad and
Dai, Shuyang and
Ray, Shayan and
Taghavi, Tara and
Yoon, Sungroh",
editor = "Yang, Yi and
Davani, Aida and
Sil, Avi and
Kumar, Anoop",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-industry.36",
doi = "10.18653/v1/2024.naacl-industry.36",
pages = "419--426",
abstract = "Task-orientated conversational agents interact with users and assist them via leveraging external APIs. A typical task-oriented conversational system can be broken down into three phases: external API selection, argument filling, and response generation. The focus of our work is the task of argument filling, which is in charge of accurately providing arguments required by the selected API. Upon comprehending the dialogue history and the pre-defined API schema, the argument filling task is expected to provide the external API with the necessary information to generate a desirable agent action. In this paper, we study the application of Large Language Models (LLMs) for the problem of API argument filling task. Our initial investigation reveals that LLMs require an additional grounding process to successfully perform argument filling, inspiring us to design training and prompting frameworks to ground their responses. Our experimental results demonstrate that when paired with proposed techniques, the argument filling performance of LLMs noticeably improves, paving a new way toward building an automated argument filling framework.",
}
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<abstract>Task-orientated conversational agents interact with users and assist them via leveraging external APIs. A typical task-oriented conversational system can be broken down into three phases: external API selection, argument filling, and response generation. The focus of our work is the task of argument filling, which is in charge of accurately providing arguments required by the selected API. Upon comprehending the dialogue history and the pre-defined API schema, the argument filling task is expected to provide the external API with the necessary information to generate a desirable agent action. In this paper, we study the application of Large Language Models (LLMs) for the problem of API argument filling task. Our initial investigation reveals that LLMs require an additional grounding process to successfully perform argument filling, inspiring us to design training and prompting frameworks to ground their responses. Our experimental results demonstrate that when paired with proposed techniques, the argument filling performance of LLMs noticeably improves, paving a new way toward building an automated argument filling framework.</abstract>
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%0 Conference Proceedings
%T LLM-based Frameworks for API Argument Filling in Task-Oriented Conversational Systems
%A Mok, Jisoo
%A Kachuee, Mohammad
%A Dai, Shuyang
%A Ray, Shayan
%A Taghavi, Tara
%A Yoon, Sungroh
%Y Yang, Yi
%Y Davani, Aida
%Y Sil, Avi
%Y Kumar, Anoop
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F mok-etal-2024-llm
%X Task-orientated conversational agents interact with users and assist them via leveraging external APIs. A typical task-oriented conversational system can be broken down into three phases: external API selection, argument filling, and response generation. The focus of our work is the task of argument filling, which is in charge of accurately providing arguments required by the selected API. Upon comprehending the dialogue history and the pre-defined API schema, the argument filling task is expected to provide the external API with the necessary information to generate a desirable agent action. In this paper, we study the application of Large Language Models (LLMs) for the problem of API argument filling task. Our initial investigation reveals that LLMs require an additional grounding process to successfully perform argument filling, inspiring us to design training and prompting frameworks to ground their responses. Our experimental results demonstrate that when paired with proposed techniques, the argument filling performance of LLMs noticeably improves, paving a new way toward building an automated argument filling framework.
%R 10.18653/v1/2024.naacl-industry.36
%U https://aclanthology.org/2024.naacl-industry.36
%U https://doi.org/10.18653/v1/2024.naacl-industry.36
%P 419-426
Markdown (Informal)
[LLM-based Frameworks for API Argument Filling in Task-Oriented Conversational Systems](https://aclanthology.org/2024.naacl-industry.36) (Mok et al., NAACL 2024)
ACL
- Jisoo Mok, Mohammad Kachuee, Shuyang Dai, Shayan Ray, Tara Taghavi, and Sungroh Yoon. 2024. LLM-based Frameworks for API Argument Filling in Task-Oriented Conversational Systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 419–426, Mexico City, Mexico. Association for Computational Linguistics.