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

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. Keywords: Large Language Models (LLMs); Financial NLP; FinLLMs; FinMA; FLARE Benchmark; FIT Dataset; Sentiment Analysis; Financial Question Answering; Stock Movement Prediction; Named Entity Recognition; Financial Text Summarization; Instruction Tuning; Financial Reasoning; Domain Adaptation.