The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks
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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, Dheeraj Sreedhar, Praveen Venkateswaran, Merve Unuvar, David Daniel Cox, Salim Roukos, Luis A. Lastras, Pavan Kapanipathi. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. 2024.
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Function calling transforms LLMs from generative models into interactive agents through structured output generation leveraging functions' abstraction mechanism, enabling external tool manipulation and access to current, domain-specific information for complex problem-solving.
“Function calling transforms LLMs from generative models into interactive agents through structured output generation leveraging functions' abstraction mechanism, enabling external tool manipulation and access to current, domain-specific information for complex problem-solving [5, 669, 335, 882, 58, 523, 1113].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [5]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredTechnical implementation involves fine-tuning (dominant method providing stable capabilities via extensive API training but requiring significant resources) and prompt engineering (flexible, resource-efficient but unstable), with approaches like 'Reverse Chain' enabling API operation via prompts, addressing challenges in large tool management.
“Technical implementation involves fine-tuning (dominant method providing stable capabilities via extensive API training but requiring significant resources) and prompt engineering (flexible, resource-efficient but unstable), with approaches like 'Reverse Chain' enabling API operation via prompts, addressing challenges in large tool management [392, 5, 1332, 791, 144, 254].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [5]
Split into 2 atoms — 0 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 1 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredTraining methodologies evolved from basic prompt-based approaches to sophisticated multi-task learning frameworks, with fine-tuning on specialized datasets through systems like ToolLLM and Granite-20B-FunctionCalling, beginning with synthetic single-tool data followed by human annotations.
“Training methodologies evolved from basic prompt-based approaches to sophisticated multi-task learning frameworks, with fine-tuning on specialized datasets through systems like ToolLLM and Granite-20B-FunctionCalling, beginning with synthetic single-tool data followed by human annotations [392, 5, 357, 777, 1235].”
From A Survey of Context Engineering for Large Language Models (2025) · cites this paper as [5]
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Citations
Cites
BERTScore: Evaluating Text Generation with BERT
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Research works printed but not linked: 19
- Kinjal Basu, Ibrahim Abdelaziz, Subhajit Chaudhury, Soham Dan, Maxwell Crouse, Asim Munawar, Sad- hana Kumaravel, Vinod Muthusamy, Pavan Kapa- nipathi, and Luis A. Lastras. 2024. Api-blend: A comprehensive corpora for training and benchmark- ing api llms. Preprint, arXiv:2402.15491. · arXiv
- Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. Preprint, arXiv:2305.14314. · arXiv
- Zhen Guo, Adriana Meza Soria, Wei Sun, Yikang Shen, and Rameswar Panda. 2024. Api pack: A massive multi-programming language dataset for api call gen- eration. Preprint, arXiv:2402.09615. · arXiv
- Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei- Fei, Fei Xia, and Brian Ichter. 2023a. Chain of code: Reasoning with a language model-augmented code emulator. arXiv preprint arXiv:2312.04474. · arXiv
- Haoran Li, Qingxiu Dong, Zhengyang Tang, Chaojun Wang, Xingxing Zhang, Haoyang Huang, Shaohan Huang, Xiaolong Huang, Zeqiang Huang, Dongdong Zhang, et al. 2024. Synthetic data (almost) from scratch: Generalized instruction tuning for language models. arXiv preprint arXiv:2402.13064. · arXiv
- Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, and Yongbin Li. 2023b. Api-bank: A comprehen- sive benchmark for tool-augmented llms. Preprint, arXiv:2304.08244. · arXiv
- Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.
- Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha Surendran, Shivdeep Singh, et al. 2024. Granite code models: A family of open foundation models for code intelli- gence. arXiv preprint arXiv:2405.04324. · arXiv
- Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022. Cross-task generaliza- tion via natural language crowdsourcing instructions. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3470-3487.
- Niklas Muennighoff, Thomas Wang, Lintang Sutawika, Adam Roberts, Stella Biderman, Teven Le Scao, M Saiful Bari, Sheng Shen, Zheng Xin Yong, Hai- ley Schoelkopf, Xiangru Tang, Dragomir Radev, Alham Fikri Aji, Khalid Almubarak, Samuel Al- banie, Zaid Alyafeai, Albert Webson, Edward Raff, and Colin Raffel. 2023. Crosslingual generaliza- tion through multitask finetuning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15991-16111, Toronto, Canada. Association for Computational Linguistics. · DOI · Paper page
- Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computa- tional Linguistics, pages 311-318.
- Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. 2023. Gorilla: Large language model connected with massive apis. arXiv preprint arXiv:2305.15334. · arXiv
- Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. 2023. Toolllm: Facilitating large language models to master 16000+ real-world apis. arXiv preprint arXiv:2307.16789. · arXiv
- Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Fi- rat, Julian Schrittwieser, et al. 2024. Gemini 1.5: Un- locking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530. · arXiv
- Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. ArXiv, abs/2302.04761. · arXiv
- Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao. 2023. Re- flexion: Language agents with verbal reinforcement learning. In Thirty-seventh Conference on Neural Information Processing Systems.
- Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D Cox, and Akash Srivas- tava. 2024. Lab: Large-scale alignment for chatbots. arXiv preprint arXiv:2403.01081. · arXiv
- Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, and Le Sun. 2023. Toolalpaca: Gener- alized tool learning for language models with 3000 simulated cases. arXiv preprint arXiv:2306.05301. · arXiv
- Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. Self-instruct: Aligning language models with self-generated instructions. In Proceed- ings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13484-13508, Toronto, Canada. Association for Computational Linguistics. · DOI · Paper page
Cited by
Sorted by citations
A Survey of Context Engineering for Large Language Models
Mei, L., Yao, J., Ge, Y., Wang, Y., Bi, B., Cai, Y., Liu, J., Li, M., Li, Z.-Z., Zhang, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2507.13334 · Cited by 281
Bridging Knowledge Gaps in LLMs via Function Calls
Basu, K. (2024) · Proceedings of the 33rd ACM International Conference on Information and Knowledge Management · doi:10.1145/3627673.3679070 · Cited by 5
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Metadata
- Authors
- Abdelaziz, I., Basu, K., Agarwal, M., Kumaravel, S., Stallone, M., Panda, R., Rizk, Y., Bhargav, G. P. S., Crouse, M., Gunasekara, C., Ikbal, S., Joshi, S., Karanam, H., Kumar, V., Munawar, A., Neelam, S., Raghu, D., Sharma, U., Soria, A. M., Sreedhar, D., Venkateswaran, P., Unuvar, M., Cox, D. D., Roukos, S., Lastras, L. A., & Kapanipathi, P.
- Affiliations
- IBM (United States) (US)
- ORCID
- Venue
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
- Published
- 2024
- Pages
- 1131-1139
- OpenAlex
- W4404783862
- Cited by
- 22
- References
- 2
- Citations by year
- 2026: 11 · 2025: 9 · 2024: 2
- FWCI
- 6.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- function calling, multi-task learning, large language models, task decomposition
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