Large Language Models in Finance: Reasoning

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing but face significant challenges in complex financial reasoning tasks that require multi-step logical inference, domain-specific knowledge, and adherence to regulatory frameworks. This paper provides a comprehensive survey and extension of advanced techniques for enhancing LLMs' reasoning capabilities, including neuro-symbolic integration, hierarchical reasoning, Chain-of-Thought prompting, ReAct frameworks, and retrieval-augmented generation. We present detailed financespecific implementations and use cases, including portfolio optimization under dynamic constraints, scenario-based stress testing, regulatory compliance analysis, and credit risk assessment, demonstrating how these techniques enable more transparent, reliable, and efficient decision-making. Our framework specifically addresses key challenges in scalability, interpretability, and bias mitigation, while advancing new directions for cognitively-inspired architectures, seamless neuro-symbolic pipelines, and continuous learning systems that adapt to evolving market conditions and regulatory requirements.

Keywords: artificial intelligence, large language models, reasoning

JEL Classification: G10, G11, G14, G17, G21, G23, G28, G32, G34, C44