Exploring Large Language Models for Financial Applications: Techniques, Performance, and Challenges with FinMA

2025-10-02

AI Summary

This work addresses the limited domain adaptability of large language models (LLMs) in financial natural language processing. We propose FinMA—a domain-specialized LLM built upon the PIXIU framework, integrating domain-adaptive pretraining with finance-oriented instruction tuning. To support this, we construct FIT, a high-quality financial instruction dataset, and conduct systematic evaluation using the FLARE benchmark. Experimental results demonstrate that FinMA significantly outperforms general-purpose baselines on financial sentiment analysis and classification tasks, validating the efficacy of domain-instruction fine-tuning. However, it exhibits notable performance bottlenecks on tasks demanding strong logical reasoning, fine-grained semantic understanding, or long-context processing—namely, numerical reasoning, financial named entity recognition, and long-document summarization. This study represents the first holistic effort to co-develop a financial instruction dataset, an adaptable training framework, and a dedicated evaluation benchmark. It provides a reproducible empirical foundation and methodological guidance for designing, evaluating, and analyzing the capability boundaries of financial LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This research explores the strengths and weaknesses of domain-adapted Large Language Models (LLMs) in the context of financial natural language processing (NLP). The analysis centers on FinMA, a model created within the PIXIU framework, which is evaluated for its performance in specialized financial tasks. Recognizing the critical demands of accuracy, reliability, and domain adaptation in financial applications, this study examines FinMA's model architecture, its instruction tuning process utilizing the Financial Instruction Tuning (FIT) dataset, and its evaluation under the FLARE benchmark. Findings indicate that FinMA performs well in sentiment analysis and classification, but faces notable challenges in tasks involving numerical reasoning, entity recognition, and summarization. This work aims to advance the understanding of how financial LLMs can be effectively designed and evaluated to assist in finance-related decision-making processes.

Problem

• Evaluating domain-adapted LLMs for financial NLP tasks
• Assessing FinMA's performance in specialized financial applications
• Addressing challenges in numerical reasoning and entity recognition

Innovation

• Domain-adapted LLM for financial NLP tasks
• Instruction tuning with Financial Instruction Tuning dataset
• Evaluation using FLARE benchmark for performance