Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Instruct-FinGPT enhances financial sentiment analysis by instruction tuning LLMs, outperforming state-of-the-art models in numerical and contextual understanding.
What it examines
The paper introduces Instruct-FinGPT, a method for financial sentiment analysis by instruction tuning general-purpose large language models (LLMs) to improve numerical sensitivity and contextual understanding, addressing limitations in existing models.
What it concludes
The research shows that instruction tuning LLMs can significantly enhance financial sentiment analysis. Potential applications include improved market prediction tools and financial news analysis. Future research could explore broader financial tasks and further refine instruction tuning methods.
Evidence objects
The research shows that instruction tuning LLMs can significantly enhance financial sentiment analysis. Potential applications include improved market prediction tools and financial news analysis. Future research could explore broader financial tasks and further refine instruction tuning methods.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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… ▽ More 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. △ Less
Source row: 1104 · abstract type: unknown