Bridging Language Models and Financial Analysis

TL;DR

The paper addresses the gap between rapid LLM development and practical adoption in finance. It surveys how datasets and models handle financial textual data and outlines six application areas, from trading to lab experiments. It proposes future directions such as prompting strategies, retrieval-augmented reasoning, tool augmentation, multimodal capabilities, and model blending, while candidly discussing limitations like hallucinations and mathematical reasoning. The work provides a structured, actionable resource for researchers and practitioners to advance responsible, effective LLM-enabled finance.

Abstract

The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly within the financial sector. Financial data is often embedded in intricate relationships across textual content, numerical tables, and visual charts, posing challenges that traditional methods struggle to address effectively. However, the emergence of LLMs offers new pathways for processing and analyzing this multifaceted data with increased efficiency and insight. Despite the fast pace of innovation in LLM research, there remains a significant gap in their practical adoption within the finance industry, where cautious integration and long-term validation are prioritized. This disparity has led to a slower implementation of emerging LLM techniques, despite their immense potential in financial applications. As a result, many of the latest advancements in LLM technology remain underexplored or not fully utilized in this domain. This survey seeks to bridge this gap by providing a comprehensive overview of recent developments in LLM research and examining their applicability to the financial sector. Building on previous survey literature, we highlight several novel LLM methodologies, exploring their distinctive capabilities and their potential relevance to financial data analysis. By synthesizing insights from a broad range of studies, this paper aims to serve as a valuable resource for researchers and practitioners, offering direction on promising research avenues and outlining future opportunities for advancing LLM applications in finance.