Paper: arXiv 2306.12659

Abstract

Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and fine-tuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital.

Complexity vs Empirical Score

  • Math Complexity: 1.0/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on practical instruction tuning of LLMs with minimal mathematical formalism, relying heavily on experimental benchmarking against SOTA models and cost-effectiveness metrics.

Research Flowchart

  flowchart TD
  A["Research Goal: Improve Financial Sentiment Analysis<br>for Numerical & Contextual Understanding"] --> B["Method: Instruction Tuning"]
  B --> C["Input: Supervised Financial Sentiment Data"]
  C --> D["Process: Transform Data into Instruction Format"]
  D --> E["Process: Fine-Tune General-Purpose LLM<br>e.g., LLaMA or GPT"]
  E --> F["Outcomes: Instruct-FinGPT Model"]
  F --> G["Key Findings: Outperforms SOTA & LLMs<br>Superior Numerical/Contextual Comprehension"]