FINancial Task

Financial task research focuses on leveraging advanced machine learning models, particularly large language models (LLMs) and deep reinforcement learning (RL) algorithms, to improve financial decision-making and analysis. Current efforts concentrate on enhancing model accuracy and robustness for tasks like financial question answering, stock prediction, portfolio management, and risk assessment, often employing techniques like fine-tuning, data fusion, and multi-agent systems. These advancements aim to improve the efficiency and accuracy of financial processes, leading to better investment strategies, risk management, and regulatory compliance. The field is actively developing new benchmarks and datasets to facilitate rigorous evaluation and comparison of different approaches.