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.- Anthology ID:
- 2024.emnlp-industry.105
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, US
- Editors:
- Franck Dernoncourt, Daniel Preoţiuc-Pietro, Anastasia Shimorina
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1433–1443
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/2024.emnlp-industry.105/
- DOI:
- 10.18653/v1/2024.emnlp-industry.105
- Cite (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.
- Cite (Informal):
- RESTful-Llama: Connecting User Queries to RESTful APIs (Xu et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/2024.emnlp-industry.105.pdf