REVIEW 5 cited by
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow evaluations, making them less suited for real-world application. To address this, we introduce \textit{Open-FinLLMs}, the first open-source multimodal financial LLMs designed to handle diverse tasks across text, tabular, time-series, and chart data, excelling in zero-shot, few-shot, and fine-tuning settings. The suite includes FinLLaMA, pre-trained on a comprehensive 52-billion-token corpus; FinLLaMA-Instruct, fine-tuned with 573K financial instructions; and FinLLaVA, enhanced with 1.43M multimodal tuning pairs for strong cross-modal reasoning. We comprehensively evaluate Open-FinLLMs across 14 financial tasks, 30 datasets, and 4 multimodal tasks in zero-shot, few-shot, and supervised fine-tuning settings, introducing two new multimodal evaluation datasets. Our results show that Open-FinLLMs outperforms afvanced financial and general LLMs such as GPT-4, across financial NLP, decision-making, and multi-modal tasks, highlighting their potential to tackle real-world challenges. To foster innovation and collaboration across academia and industry, we release all codes (https://anonymous.4open.science/r/PIXIU2-0D70/B1D7/LICENSE) and models under OSI-approved licenses.
Forward citations
Cited by 5 Pith papers
-
FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering
FinSAgent improves financial filing QA by conditioning sub-queries on a summary of the local corpus and gating semantic reranking with a learned validity signal, beating baseline systems on five benchmarks.
-
FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domain
FinGAIA is a 407-task Chinese financial agent benchmark where the best agent, ChatGPT DeepResearch, scores 48.9%, far below financial experts at 84.7%.
-
TabReason: A Reinforcement Learning-Enhanced Reasoning LLM for Explainable Tabular Data Prediction
Applying GRPO reinforcement learning with format and accuracy rewards to a 1.5B LLM yields high weighted F1 on financial tabular benchmarks, but near-zero MCC on imbalanced datasets and unvalidated explanations.
-
RKEFino1: A Regulation Knowledge-Enhanced Large Language Model
RKEFino1, a fine-tuned financial LLM with regulatory knowledge, reports gains over Fino1 on DRR tasks, but the evaluation is limited and lacks external benchmarks.
-
Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy
A systematic review and taxonomy that organizes LLM-based credit risk research by model architecture, data modality, explainability mechanism, and application domain.
Discussion (0). Sign in to comment.