Exploring Large Language Models for Financial Applications: Techniques, Performance, and Challenges with FinMA
It addresses the problem of adapting LLMs for financial applications to improve decision-making, but is incremental as it builds on existing frameworks like PIXIU and benchmarks like FLARE.
This research evaluated FinMA, a domain-adapted Large Language Model for financial NLP, finding it performs well in sentiment analysis and classification but struggles with numerical reasoning, entity recognition, and summarization tasks.
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.