Teaching a Language Model to Speak the Language of Tools
External tool integration through function-calling is essential for practical
language model applications, yet most multilingual models lack reliable
tool-use capabilities in non-English languages. Even state-of-the-art
multilingual models struggle with determining when to use tools and generating
the structured outputs required for function calls, often exhibiting language
confusion when prompted in lower-resource languages. This work presents a
methodology for adapting existing language models to enable robust tool use in
any target language, using Bulgarian as a case study. The approach involves
continued training of the BgGPT model series (2.6B, 9B, 27B parameters) on a
novel bilingual dataset of 10,035 function-calling examples designed to support
standardized protocols like MCP (Model Context Protocol). The research
introduces TUCAN (Tool-Using Capable Assistant Navigator), which achieves up to
28.75% improvement in function-calling accuracy over base models while
preserving core language understanding, as verified on established Bulgarian
benchmarks. Beyond accuracy gains, TUCAN models demonstrate production-ready
response formatting with clean, parsable function calls, contrasting with the
verbose and inconsistent outputs of base models. The models, evaluation
framework, and dataset are released to enable replication for other languages.
This work demonstrates a practical approach for extending tool-augmented
capabilities beyond English-centric systems.
686350a9588cea0da970c8d4
Simeon Emanuilov
claimed_verified
2025-07-01T10:13:22.926Z
645dbaa6f5760d1530d7580d
https://cdn-avatars.huggingface.co/v1/production/uploads/645dbaa6f5760d1530d7580d/Bqob8arLZoHIgMwNZpL9I.jpeg
Simeon Emanuilov
user
s-emanuilov
Teaching a Language Model to Speak the Language of Tools
External tool integration through function-calling is essential for practical
language model applications, yet most multilingual models lack reliable
tool-use capabilities in non-English languages. Even state-of-the-art
multilingual models struggle with determining when to use tools and generating
the structured outputs required for function calls, often exhibiting language
confusion when prompted in lower-resource languages. This work presents a
methodology for adapting existing language models to enable robust tool use in
any target language, using Bulgarian as a case study. The approach involves
continued training of the BgGPT model series (2.6B, 9B, 27B parameters) on a
novel bilingual dataset of 10,035 function-calling examples designed to support
standardized protocols like MCP (Model Context Protocol). The research
introduces TUCAN (Tool-Using Capable Assistant Navigator), which achieves up to
28.75% improvement in function-calling accuracy over base models while
preserving core language understanding, as verified on established Bulgarian
benchmarks. Beyond accuracy gains, TUCAN models demonstrate production-ready
response formatting with clean, parsable function calls, contrasting with the
verbose and inconsistent outputs of base models. The models, evaluation
framework, and dataset are released to enable replication for other languages.
This work demonstrates a practical approach for extending tool-augmented
capabilities beyond English-centric systems.
A methodology is presented to adapt language models for robust tool use in any target language, using Bulgarian as a case study, and introduces TUCAN, which improves function-calling accuracy while maintaining language understanding.
function-calling multilingual models tool-use capabilities BgGPT bilingual dataset MCP TUCAN function-calling accuracy response formatting