Re(Visiting) Large Language Models inFinance

ArXiv ID: ssrn-4963618 “View on arXiv”

Authors: Unknown

Abstract

This study evaluates the effectiveness of specialised large language models (LLMs) developed for accounting and finance. Empirical analysis demonstrates that th

Keywords: Large Language Models, Accounting, Financial Analysis, Natural Language Processing

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper demonstrates high empirical rigor through extensive data handling, robustness checks, and a clear backtest-ready methodology (out-of-sample testing, look-ahead bias mitigation). Math complexity is moderate-to-high due to the advanced transformer architectures and the statistical foundations of LLMs, though the focus is on applied implementation rather than deep theoretical derivations.

  flowchart TD
    A["Research Goal: Assess effectiveness of specialised LLMs for Accounting & Finance"] --> B["Methodology: Empirical Analysis of FinanceBench & FinEval"]
    B --> C["Computational Process: Instruction-Tuning & In-Context Learning"]
    C --> D{"Key Findings"}
    D --> E["Specialised Models outperform general LLMs"]
    D --> F["Instruction-tuning significantly boosts financial accuracy"]
    D --> G["Task-specific prompting (ICL) improves performance"]