Paper: arXiv 2503.22693

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.

Complexity vs Empirical Score

  • Math Complexity: 3.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: This paper is a survey of existing research and lacks novel mathematical derivations or new empirical backtests, focusing instead on reviewing methodologies and discussing future directions for applying LLMs in finance.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Bridge LLM & Finance<br>Integration Gap"] --> B["Methodology:<br>Comprehensive Literature Survey"]
  B --> C["Data/Inputs:<br>Recent LLM Research<br>Financial Datasets<br>Multi-modal Sources"]
  C --> D["Computational Process:<br>Analysis of<br>Novel LLM Methodologies"]
  D --> E["Key Findings:<br>Identified Applicable Techniques<br>Future Research Avenues"]
  E --> F["Outcome:<br>Resource for Researchers &<br>Practitioners in Finance"]

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32 of 311 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.