Teaching a Language Model to Speak the Language of Tools
- URL: http://arxiv.org/abs/2506.23394v1
- Date: Sun, 29 Jun 2025 20:47:27 GMT
- Title: Teaching a Language Model to Speak the Language of Tools
- Authors: Simeon Emanuilov,
- Abstract summary: This work presents a methodology for adapting existing language models to enable robust tool use in any target language.<n>The research introduces TUCAN, which achieves up to 28.75% improvement in function-calling accuracy over base models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs [54.59207567677249]
Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
arXiv Detail & Related papers (2025-05-23T20:28:31Z) - Enhancing Multilingual Language Models for Code-Switched Input Data [0.0]
This research investigates if pre-training Multilingual BERT (mBERT) on code-switched datasets improves the model's performance on critical NLP tasks.<n>We use a dataset of Spanglish tweets for pre-training and evaluate the pre-trained model against a baseline model.<n>Our findings show that our pre-trained mBERT model outperforms or matches the baseline model in the given tasks, with the most significant improvements seen for parts of speech tagging.
arXiv Detail & Related papers (2025-03-11T02:49:41Z) - BgGPT 1.0: Extending English-centric LLMs to other languages [12.867025651644692]
We present BgGPT-Gemma-2-27B-Instruct and BgGPT-Gemma-2-9B-Instruct: continually pretrained and fine-tuned versions of Google's Gemma-2 models.<n>Our models demonstrate strong performance in Bulgarian language tasks, setting a new standard for language-specific AI models.
arXiv Detail & Related papers (2024-12-14T16:49:52Z) - FunctionChat-Bench: Comprehensive Evaluation of Language Models' Generative Capabilities in Korean Tool-use Dialogs [4.406769771178207]
This study investigates language models' generative capabilities in tool-use dialogs.
We categorize the models' outputs in tool-use dialogs into four distinct types: Tool Call, Answer Completion, Slot Question, and Relevance Detection.
Using this benchmark, we evaluate several language models that support function calling.
arXiv Detail & Related papers (2024-11-21T11:59:13Z) - Investigating Language-Specific Calibration For Pruning Multilingual Large Language Models [11.421452042888523]
We compare different calibration languages for pruning multilingual models across diverse languages, tasks, models, and SotA pruning techniques.
Our results offer practical suggestions, for example, calibrating in the target language can efficiently retain the language modeling capability but does not necessarily benefit downstream tasks.
arXiv Detail & Related papers (2024-08-26T16:29:13Z) - CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models [59.91221728187576]
This paper introduces the CMU Linguistic Linguistic Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models.
CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages.
arXiv Detail & Related papers (2024-04-03T02:21:46Z) - Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally.
Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
arXiv Detail & Related papers (2023-06-13T08:08:08Z) - Improving Massively Multilingual ASR With Auxiliary CTC Objectives [40.10307386370194]
We introduce our work on improving performance on FLEURS, a 102-language open ASR benchmark.
We investigate techniques inspired from recent Connectionist Temporal Classification ( CTC) studies to help the model handle the large number of languages.
Our state-of-the-art systems using self-supervised models with the Conformer architecture improve over the results of prior work on FLEURS by a relative 28.4% CER.
arXiv Detail & Related papers (2023-02-24T18:59:51Z) - Are Multilingual Models Effective in Code-Switching? [57.78477547424949]
We study the effectiveness of multilingual language models to understand their capability and adaptability to the mixed-language setting.
Our findings suggest that pre-trained multilingual models do not necessarily guarantee high-quality representations on code-switching.
arXiv Detail & Related papers (2021-03-24T16:20:02Z) - Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language
Model [58.27176041092891]
Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements.
We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations.
Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
arXiv Detail & Related papers (2020-11-23T16:00:42Z) - Learning to Scale Multilingual Representations for Vision-Language Tasks [51.27839182889422]
The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date.
We evaluate on multilingual image-sentence retrieval and outperform prior work by 3-4% with less than 1/5th the training parameters compared to other word embedding methods.
arXiv Detail & Related papers (2020-04-09T01:03:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.