@inproceedings{basu-etal-2024-api,
title = "{API}-{BLEND}: A Comprehensive Corpora for Training and Benchmarking {API} {LLM}s",
author = "Basu, Kinjal and
Abdelaziz, Ibrahim and
Chaudhury, Subhajit and
Dan, Soham and
Crouse, Maxwell and
Munawar, Asim and
Austel, Vernon and
Kumaravel, Sadhana and
Muthusamy, Vinod and
Kapanipathi, Pavan and
Lastras, Luis",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.694",
pages = "12859--12870",
abstract = "There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predominant strategies for addressing this challenge. The first has focused on synthetic data generation techniques, while the second has involved curating task-adjacent datasets which can be transformed into API / Tool-based tasks. In this paper, we focus on the task of identifying, curating, and transforming existing datasets and, in turn, introduce API-BLEND, a large corpora for training and systematic testing of tool-augmented LLMs. The datasets mimic real-world scenarios involving API-tasks such as API / tool detection, slot filling, and sequencing of the detected APIs. We demonstrate the utility of the API-BLEND dataset for both training and benchmarking purposes.",
}
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%0 Conference Proceedings
%T API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs
%A Basu, Kinjal
%A Abdelaziz, Ibrahim
%A Chaudhury, Subhajit
%A Dan, Soham
%A Crouse, Maxwell
%A Munawar, Asim
%A Austel, Vernon
%A Kumaravel, Sadhana
%A Muthusamy, Vinod
%A Kapanipathi, Pavan
%A Lastras, Luis
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F basu-etal-2024-api
%X There is a growing need for Large Language Models (LLMs) to effectively use tools and external Application Programming Interfaces (APIs) to plan and complete tasks. As such, there is tremendous interest in methods that can acquire sufficient quantities of train and test data that involve calls to tools / APIs. Two lines of research have emerged as the predominant strategies for addressing this challenge. The first has focused on synthetic data generation techniques, while the second has involved curating task-adjacent datasets which can be transformed into API / Tool-based tasks. In this paper, we focus on the task of identifying, curating, and transforming existing datasets and, in turn, introduce API-BLEND, a large corpora for training and systematic testing of tool-augmented LLMs. The datasets mimic real-world scenarios involving API-tasks such as API / tool detection, slot filling, and sequencing of the detected APIs. We demonstrate the utility of the API-BLEND dataset for both training and benchmarking purposes.
%U https://aclanthology.org/2024.acl-long.694
%P 12859-12870
Markdown (Informal)
[API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs](https://aclanthology.org/2024.acl-long.694) (Basu et al., ACL 2024)
ACL
- Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Vernon Austel, Sadhana Kumaravel, Vinod Muthusamy, Pavan Kapanipathi, and Luis Lastras. 2024. API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12859–12870, Bangkok, Thailand. Association for Computational Linguistics.