@inproceedings{xu-etal-2024-restful,
title = "{REST}ful-Llama: Connecting User Queries to {REST}ful {API}s",
author = "Xu, Han and
Zhao, Ruining and
Wang, Jindong and
Chen, Haipeng",
editor = "Dernoncourt, Franck and
Preo{\c{t}}iuc-Pietro, Daniel and
Shimorina, Anastasia",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2024",
address = "Miami, Florida, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-industry.105",
pages = "1433--1443",
abstract = "Recent advancements in Large Language Models (LLMs) have showcased exceptional performance in zero-shot learning and reasoning tasks. However, integrating these models with external tools - a crucial need for real-world applications - remains a significant challenge. We propose RESTful-Llama, a novel framework designed to enable Llama 3.1 to transform natural language instructions into effective RESTful API calls. To enhance the fine-tuning process, we introduce DOC{\_}Mine, a method to generate fine-tuning datasets from public API documentation. RESTful-Llama distinguishes itself by enabling open-source LLMs to efficiently interact with and adapt to any REST API system. Experiments demonstrate a 31.9{\%} improvement in robustness and a 2.33x increase in efficiency compared to existing methods.",
}
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<abstract>Recent advancements in Large Language Models (LLMs) have showcased exceptional performance in zero-shot learning and reasoning tasks. However, integrating these models with external tools - a crucial need for real-world applications - remains a significant challenge. We propose RESTful-Llama, a novel framework designed to enable Llama 3.1 to transform natural language instructions into effective RESTful API calls. To enhance the fine-tuning process, we introduce DOC_Mine, a method to generate fine-tuning datasets from public API documentation. RESTful-Llama distinguishes itself by enabling open-source LLMs to efficiently interact with and adapt to any REST API system. Experiments demonstrate a 31.9% improvement in robustness and a 2.33x increase in efficiency compared to existing methods.</abstract>
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%0 Conference Proceedings
%T RESTful-Llama: Connecting User Queries to RESTful APIs
%A Xu, Han
%A Zhao, Ruining
%A Wang, Jindong
%A Chen, Haipeng
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F xu-etal-2024-restful
%X Recent advancements in Large Language Models (LLMs) have showcased exceptional performance in zero-shot learning and reasoning tasks. However, integrating these models with external tools - a crucial need for real-world applications - remains a significant challenge. We propose RESTful-Llama, a novel framework designed to enable Llama 3.1 to transform natural language instructions into effective RESTful API calls. To enhance the fine-tuning process, we introduce DOC_Mine, a method to generate fine-tuning datasets from public API documentation. RESTful-Llama distinguishes itself by enabling open-source LLMs to efficiently interact with and adapt to any REST API system. Experiments demonstrate a 31.9% improvement in robustness and a 2.33x increase in efficiency compared to existing methods.
%U https://aclanthology.org/2024.emnlp-industry.105
%P 1433-1443
Markdown (Informal)
[RESTful-Llama: Connecting User Queries to RESTful APIs](https://aclanthology.org/2024.emnlp-industry.105) (Xu et al., EMNLP 2024)
ACL
- Han Xu, Ruining Zhao, Jindong Wang, and Haipeng Chen. 2024. RESTful-Llama: Connecting User Queries to RESTful APIs. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1433–1443, Miami, Florida, US. Association for Computational Linguistics.