Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908558436597760 |
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| author | Ersoy, Asim Altinisik, Enes Sencar, Husrev Taha Darwish, Kareem |
| author_facet | Ersoy, Asim Altinisik, Enes Sencar, Husrev Taha Darwish, Kareem |
| contents | Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resources for tool calling are predominantly English-centric, leaving a gap in our understanding of how to enable this functionality for other languages, such as Arabic. This paper investigates three key research questions: (1) the necessity of in-language (Arabic) tool-calling data versus relying on cross-lingual transfer, (2) the effect of general-purpose instruction tuning on tool-calling performance, and (3) the value of fine-tuning on specific, high-priority tools. To address these questions, we conduct extensive experiments using base and post-trained variants of an open-weight Arabic LLM. To enable this study, we bridge the resource gap by translating and adapting two open-source tool-calling datasets into Arabic. Our findings provide crucial insights into the optimal strategies for developing robust tool-augmented agents for Arabic. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning Ersoy, Asim Altinisik, Enes Sencar, Husrev Taha Darwish, Kareem Computation and Language Tool calling is a critical capability that allows Large Language Models (LLMs) to interact with external systems, significantly expanding their utility. However, research and resources for tool calling are predominantly English-centric, leaving a gap in our understanding of how to enable this functionality for other languages, such as Arabic. This paper investigates three key research questions: (1) the necessity of in-language (Arabic) tool-calling data versus relying on cross-lingual transfer, (2) the effect of general-purpose instruction tuning on tool-calling performance, and (3) the value of fine-tuning on specific, high-priority tools. To address these questions, we conduct extensive experiments using base and post-trained variants of an open-weight Arabic LLM. To enable this study, we bridge the resource gap by translating and adapting two open-source tool-calling datasets into Arabic. Our findings provide crucial insights into the optimal strategies for developing robust tool-augmented agents for Arabic. |
| title | Tool Calling for Arabic LLMs: Data Strategies and Instruction Tuning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.20957 |