FinGPT: Instruction Tuning Benchmark for Open-Source Large Language Models in Financial Datasets

ArXiv ID: 2310.04793 “View on arXiv”

Authors: Unknown

Abstract

In the swiftly expanding domain of Natural Language Processing (NLP), the potential of GPT-based models for the financial sector is increasingly evident. However, the integration of these models with financial datasets presents challenges, notably in determining their adeptness and relevance. This paper introduces a distinctive approach anchored in the Instruction Tuning paradigm for open-source large language models, specifically adapted for financial contexts. Through this methodology, we capitalize on the interoperability of open-source models, ensuring a seamless and transparent integration. We begin by explaining the Instruction Tuning paradigm, highlighting its effectiveness for immediate integration. The paper presents a benchmarking scheme designed for end-to-end training and testing, employing a cost-effective progression. Firstly, we assess basic competencies and fundamental tasks, such as Named Entity Recognition (NER) and sentiment analysis to enhance specialization. Next, we delve into a comprehensive model, executing multi-task operations by amalgamating all instructional tunings to examine versatility. Finally, we explore the zero-shot capabilities by earmarking unseen tasks and incorporating novel datasets to understand adaptability in uncharted terrains. Such a paradigm fortifies the principles of openness and reproducibility, laying a robust foundation for future investigations in open-source financial large language models (FinLLMs).

Keywords: Instruction Tuning, FinLLMs (Financial Large Language Models), Named Entity Recognition (NER), Sentiment analysis, Zero-shot learning, Equities

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper focuses on a practical benchmarking scheme with specific tasks, datasets, and model implementations, indicating strong empirical rigor; however, it lacks advanced mathematical derivations or complex modeling, relying instead on established NLP techniques like instruction tuning.

  flowchart TD
    A["Research Goal<br>FinGPT: Instruction Tuning Benchmark<br>for Open-Source FinLLMs"] --> B["Methodology<br>Instruction Tuning Paradigm"]
    B --> C["Data/Inputs<br>Financial Datasets &<br>Instructional Prompts"]
    
    C --> D1["Phase 1: Basic Competencies<br>NER & Sentiment Analysis"]
    C --> D2["Phase 2: Multi-Task Operations<br>Combined Instructional Tunings"]
    C --> D3["Phase 3: Zero-Shot Capabilities<br>Unseen Tasks & Novel Datasets"]
    
    D1 --> E["Computational Process<br>End-to-End Training & Testing<br>Cost-Effective Progression"]
    D2 --> E
    D3 --> E
    
    E --> F["Key Findings/Outcomes<br>Specialization, Versatility, & Adaptability<br>Fortified Openness & Reproducibility"]