Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of
  General-Purpose Large Language Models
Boyu Zhang Hongyang Yang Xiao-Yang Liu
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.