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FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models

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arxiv 2402.10986 v3 pith:SXTFJDHD submitted 2024-02-16 cs.CL cs.AI

Gagan Bhatia , El Moatez Billah Nagoudi , Hasan Cavusoglu , Muhammad Abdul-Mageed This is my paper
classification cs.CLcs.AI
keywords fintralfinanciallargetasksanalysisdatasetsgithubgpt-4
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. FinTral integrates textual, numerical, tabular, and image data. We enhance FinTral with domain-specific pretraining, instruction fine-tuning, and RLAIF training by exploiting a large collection of textual and visual datasets we curate for this work. We also introduce an extensive benchmark featuring nine tasks and 25 datasets for evaluation, including hallucinations in the financial domain. Our FinTral model trained with direct preference optimization employing advanced Tools and Retrieval methods, dubbed FinTral-DPO-T&R, demonstrates an exceptional zero-shot performance. It outperforms ChatGPT-3.5 in all tasks and surpasses GPT-4 in five out of nine tasks, marking a significant advancement in AI-driven financial technology. We also demonstrate that FinTral has the potential to excel in real-time analysis and decision-making in diverse financial contexts. The GitHub repository for FinTral is available at \url{https://github.com/UBC-NLP/fintral}.

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Cited by 3 Pith papers

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  1. CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model

    cs.CL 2025-06 conditional novelty 6.0 of 10

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    On financial sentiment classification, zero-shot LLMs match human labels better without chain-of-thought reasoning than with it.

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    A survey that categorizes continual learning methods for generative models into architecture-based, regularization-based, and replay-based paradigms across four model families.

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