The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909898588028928 |
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| author | Caillaut, Gaëtan Qader, Raheel Liu, Jingshu Nakhlé, Mariam Sadoune, Arezki Ahmim, Massinissa Barthelemy, Jean-Gabriel |
| author_facet | Caillaut, Gaëtan Qader, Raheel Liu, Jingshu Nakhlé, Mariam Sadoune, Arezki Ahmim, Massinissa Barthelemy, Jean-Gabriel |
| contents | The financial industry's growing demand for advanced natural language processing (NLP) capabilities has highlighted the limitations of generalist large language models (LLMs) in handling domain-specific financial tasks. To address this gap, we introduce the LLM Pro Finance Suite, a collection of five instruction-tuned LLMs (ranging from 8B to 70B parameters) specifically designed for financial applications. Our approach focuses on enhancing generalist instruction-tuned models, leveraging their existing strengths in instruction following, reasoning, and toxicity control, while fine-tuning them on a curated, high-quality financial corpus comprising over 50% finance-related data in English, French, and German.
We evaluate the LLM Pro Finance Suite on a comprehensive financial benchmark suite, demonstrating consistent improvement over state-of-the-art baselines in finance-oriented tasks and financial translation. Notably, our models maintain the strong general-domain capabilities of their base models, ensuring reliable performance across non-specialized tasks. This dual proficiency, enhanced financial expertise without compromise on general abilities, makes the LLM Pro Finance Suite an ideal drop-in replacement for existing LLMs in financial workflows, offering improved domain-specific performance while preserving overall versatility. We publicly release two 8B-parameters models to foster future research and development in financial NLP applications: https://huggingface.co/collections/DragonLLM/llm-open-finance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08621 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications Caillaut, Gaëtan Qader, Raheel Liu, Jingshu Nakhlé, Mariam Sadoune, Arezki Ahmim, Massinissa Barthelemy, Jean-Gabriel Statistical Finance Artificial Intelligence Computational Finance The financial industry's growing demand for advanced natural language processing (NLP) capabilities has highlighted the limitations of generalist large language models (LLMs) in handling domain-specific financial tasks. To address this gap, we introduce the LLM Pro Finance Suite, a collection of five instruction-tuned LLMs (ranging from 8B to 70B parameters) specifically designed for financial applications. Our approach focuses on enhancing generalist instruction-tuned models, leveraging their existing strengths in instruction following, reasoning, and toxicity control, while fine-tuning them on a curated, high-quality financial corpus comprising over 50% finance-related data in English, French, and German. We evaluate the LLM Pro Finance Suite on a comprehensive financial benchmark suite, demonstrating consistent improvement over state-of-the-art baselines in finance-oriented tasks and financial translation. Notably, our models maintain the strong general-domain capabilities of their base models, ensuring reliable performance across non-specialized tasks. This dual proficiency, enhanced financial expertise without compromise on general abilities, makes the LLM Pro Finance Suite an ideal drop-in replacement for existing LLMs in financial workflows, offering improved domain-specific performance while preserving overall versatility. We publicly release two 8B-parameters models to foster future research and development in financial NLP applications: https://huggingface.co/collections/DragonLLM/llm-open-finance. |
| title | The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications |
| topic | Statistical Finance Artificial Intelligence Computational Finance |
| url | https://arxiv.org/abs/2511.08621 |