FinBERT - A Large Language Model for Extracting Information from Financial Text

ArXiv ID: ssrn-3910214 “View on arXiv”

Authors: Unknown

Abstract

We develop FinBERT, a state-of-the-art large language model that adapts to the finance domain. We show that FinBERT incorporates finance knowledge and can bette

Keywords: FinBERT, Natural Language Processing, Large Language Models, Financial Text Analysis, Technology/AI

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper focuses on fine-tuning a pre-existing transformer model (FinBERT) with specific financial datasets, which is primarily an empirical, implementation-heavy task with significant data preparation and evaluation metrics, while the underlying mathematics is standard deep learning rather than novel or dense derivations.

  flowchart TD
    A["Research Goal:<br>Create domain-adapted LLM for finance"] --> B["Data:<br>Financial Documents & Corpora"]
    B --> C["Preprocessing:<br>Tokenization & Formatting"]
    C --> D["Core Methodology:<br>BERT Architecture Adaptation"]
    D --> E["Training:<br>Domain-specific Fine-tuning"]
    E --> F["Evaluation:<br>Benchmark Testing"]
    F --> G["Outcome:<br>FinBERT Model"]
    F --> H["Outcome:<br>Improved Performance vs. General LLMs"]
    G --> I["Final Result:<br>State-of-the-art Financial NLP"]
    H --> I