FinTral: A Family of GPT-4 Level Multimodal Financial Large Language
  Models
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.
65d425d4345d14c0c4dfa5d7
Gagan Bhatia
claimed_verified
2024-02-20T08:38:46.970Z
60394599033b61166496163b
https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
Gagan Bhatia
user
gagan3012
65d425d4345d14c0c4dfa5d8
El Moatez Billah Nagoudi
admin_assigned
2024-02-20T11:02:55.214Z
5fed19cf1dd5c1d397248f41
https://cdn-avatars.huggingface.co/v1/production/uploads/1625528181158-5fed19cf1dd5c1d397248f41.png
Nagoudi ElMoatez Billah
user
m-nagoudi
65d425d4345d14c0c4dfa5d9
Hasan Cavusoglu
65d425d4345d14c0c4dfa5da
Muhammad Abdul-Mageed
claimed_verified
2024-02-20T08:38:44.909Z
5feeae8dd8e0b2d24ed258cc
/avatars/1d66840db4ed6dc12bdee7ba8168ea9f.svg
Muhammad Abdul-Mageed
user
mageed
FinTral: A Family of GPT-4 Level Multimodal Financial Large Language
  Models
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.
FinTral, a multimodal LLM enhanced through domain-specific pretraining, instruction fine-tuning, and RLAIF, outperforms ChatGPT-3.5 and GPT-4 in financial analysis tasks with exceptional zero-shot performance.
multimodal large language models LLMs Mistral-7b domain-specific pretraining instruction fine-tuning RLAIF training textual datasets visual datasets benchmark hallucinations direct preference optimization zero-shot performance real-time analysis decision-making