@inproceedings{abdelaziz-etal-2024-granite,
title = "Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks",
author = "Abdelaziz, Ibrahim and
Basu, Kinjal and
Agarwal, Mayank and
Kumaravel, Sadhana and
Stallone, Matthew and
Panda, Rameswar and
Rizk, Yara and
Bhargav, G P Shrivatsa and
Crouse, Maxwell and
Gunasekara, Chulaka and
Ikbal, Shajith and
Joshi, Sachindra and
Karanam, Hima and
Kumar, Vineet and
Munawar, Asim and
Neelam, Sumit and
Raghu, Dinesh and
Sharma, Udit and
Soria, Adriana Meza and
Sreedhar, Dheeraj and
Venkateswaran, Praveen and
Unuvar, Merve and
Cox, David Daniel and
Roukos, Salim and
Lastras, Luis A. and
Kapanipathi, Pavan",
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.85",
pages = "1131--1139",
abstract = "An emergent research trend explores the use of Large Language Models (LLMs) as the backbone of agentic systems (e.g., SWE-Bench, Agent-Bench). To fulfill LLMs{'} potential as autonomous agents, they must be able to identify, call, and interact with a variety of external tools and application program interfaces (APIs). This capability of LLMs, commonly termed function calling, leads to a myriad of advantages such as access to current and domain-specific information in databases and the outsourcing of tasks that can be reliably performed by tools. In this work, we introduce Granite-20B-FunctionCalling, a model trained using a multi-task training approach on seven fundamental tasks encompassed in function calling. Our comprehensive evaluation on multiple out-of-domain datasets, which compares Granite-20B-FunctionCalling to more than 15 other best proprietary and open models, shows that Granite-20B-FunctionCalling has better generalizability on multiple tasks across seven different evaluation benchmarks. Moreover, Granite-20B-FunctionCalling shows the best performance among all open models and ranks among the top on the Berkeley Function Calling Leaderboard (BFCL).",
}
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<abstract>An emergent research trend explores the use of Large Language Models (LLMs) as the backbone of agentic systems (e.g., SWE-Bench, Agent-Bench). To fulfill LLMs’ potential as autonomous agents, they must be able to identify, call, and interact with a variety of external tools and application program interfaces (APIs). This capability of LLMs, commonly termed function calling, leads to a myriad of advantages such as access to current and domain-specific information in databases and the outsourcing of tasks that can be reliably performed by tools. In this work, we introduce Granite-20B-FunctionCalling, a model trained using a multi-task training approach on seven fundamental tasks encompassed in function calling. Our comprehensive evaluation on multiple out-of-domain datasets, which compares Granite-20B-FunctionCalling to more than 15 other best proprietary and open models, shows that Granite-20B-FunctionCalling has better generalizability on multiple tasks across seven different evaluation benchmarks. Moreover, Granite-20B-FunctionCalling shows the best performance among all open models and ranks among the top on the Berkeley Function Calling Leaderboard (BFCL).</abstract>
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%0 Conference Proceedings
%T Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
%A Abdelaziz, Ibrahim
%A Basu, Kinjal
%A Agarwal, Mayank
%A Kumaravel, Sadhana
%A Stallone, Matthew
%A Panda, Rameswar
%A Rizk, Yara
%A Bhargav, G. P. Shrivatsa
%A Crouse, Maxwell
%A Gunasekara, Chulaka
%A Ikbal, Shajith
%A Joshi, Sachindra
%A Karanam, Hima
%A Kumar, Vineet
%A Munawar, Asim
%A Neelam, Sumit
%A Raghu, Dinesh
%A Sharma, Udit
%A Soria, Adriana Meza
%A Sreedhar, Dheeraj
%A Venkateswaran, Praveen
%A Unuvar, Merve
%A Cox, David Daniel
%A Roukos, Salim
%A Lastras, Luis A.
%A Kapanipathi, Pavan
%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 abdelaziz-etal-2024-granite
%X An emergent research trend explores the use of Large Language Models (LLMs) as the backbone of agentic systems (e.g., SWE-Bench, Agent-Bench). To fulfill LLMs’ potential as autonomous agents, they must be able to identify, call, and interact with a variety of external tools and application program interfaces (APIs). This capability of LLMs, commonly termed function calling, leads to a myriad of advantages such as access to current and domain-specific information in databases and the outsourcing of tasks that can be reliably performed by tools. In this work, we introduce Granite-20B-FunctionCalling, a model trained using a multi-task training approach on seven fundamental tasks encompassed in function calling. Our comprehensive evaluation on multiple out-of-domain datasets, which compares Granite-20B-FunctionCalling to more than 15 other best proprietary and open models, shows that Granite-20B-FunctionCalling has better generalizability on multiple tasks across seven different evaluation benchmarks. Moreover, Granite-20B-FunctionCalling shows the best performance among all open models and ranks among the top on the Berkeley Function Calling Leaderboard (BFCL).
%U https://aclanthology.org/2024.emnlp-industry.85
%P 1131-1139
Markdown (Informal)
[Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks](https://aclanthology.org/2024.emnlp-industry.85) (Abdelaziz et al., EMNLP 2024)
ACL
- Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, G P Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachindra Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Meza Soria, et al.. 2024. Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1131–1139, Miami, Florida, US. Association for Computational Linguistics.