The LLM Pro Finance Suite: Multilingual Large Language Models for Financial Applications

ArXiv ID: 2511.08621 “View on arXiv”

Authors: Gaëtan Caillaut, Raheel Qader, Jingshu Liu, Mariam Nakhlé, Arezki Sadoune, Massinissa Ahmim, Jean-Gabriel Barthelemy

Abstract

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.

Keywords: Instruction Tuning, Financial NLP, Large Language Models (LLMs), Financial Translation, Domain Adaptation, General Financial Services (NLP)

Complexity vs Empirical Score

• Math Complexity: 1.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper’s primary contribution is empirical, involving data curation, model fine-tuning on a large, structured dataset, and evaluation on a comprehensive financial benchmark suite, with links to released models. The mathematics is largely conceptual, focusing on LLM architectures and training methodologies rather than dense analytical derivations.

  flowchart TD
    A["Research Goal<br>Develop specialized LLMs for finance tasks<br>without losing general capabilities"] --> B["Methodology<br>Instruction-tune 5 LLMs (8B-70B) on curated corpus"]
    B --> C["Data & Inputs<br>High-quality financial corpus<br>English, French, German"]
    C --> D["Computational Process<br>Fine-tuning & Instruction Tuning"]
    D --> E["Evaluation<br>Benchmark suite vs. SOTA baselines"]
    E --> F["Key Outcomes<br>1. Consistent improvement in financial tasks<br>2. Retained general-domain performance<br>3. Released 8B models for research"]