Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models

• URL: http://arxiv.org/abs/2306.12659v1
• Date: Thu, 22 Jun 2023 03:56:38 GMT
• Title: Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
• Authors: Boyu Zhang, Hongyang Yang, Xiao-Yang Liu
• Abstract summary: We introduce a simple yet effective instruction tuning approach to address these issues. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models.
• Score: 18.212210748797332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications [2.2661367844871854]
  Large Language Models (LLMs) can be used in this context, but they are not finance-specific and tend to require significant computational resources. We introduce a novel approach based on the Llama 2 7B foundational model, in order to benefit from its generative nature and comprehensive language manipulation. This is achieved by fine-tuning the Llama2 7B model on a small portion of supervised financial sentiment analysis data.
  arXiv  Detail & Related papers  (2024-03-18T22:11:00Z)
• Large Language Model Adaptation for Financial Sentiment Analysis [2.0499240875882]
  Generalist language models tend to fall short in tasks specifically tailored for finance. Two foundation models with less than 1.5B parameters have been adapted using a wide range of strategies. We show that small LLMs have comparable performance to larger scale models, while being more efficient in terms of parameters and data.
  arXiv  Detail & Related papers  (2024-01-26T11:04:01Z)
• Revolutionizing Finance with LLMs: An Overview of Applications and Insights [47.11391223936608]
  Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice.
  arXiv  Detail & Related papers  (2024-01-22T01:06:17Z)
• Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models [11.154814189699735]
  Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks. We introduce a retrieval-augmented LLMs framework for financial sentiment analysis. Our approach achieves 15% to 48% performance gain in accuracy and F1 score.
  arXiv  Detail & Related papers  (2023-10-06T05:40:23Z)
• PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance [63.51545277822702]
  PIXIU is a comprehensive framework including the first financial large language model (LLMs) based on fine-tuning LLaMA with instruction data. We propose FinMA by fine-tuning LLaMA with the constructed dataset to be able to follow instructions for various financial tasks. We conduct a detailed analysis of FinMA and several existing LLMs, uncovering their strengths and weaknesses in handling critical financial tasks.
  arXiv  Detail & Related papers  (2023-06-08T14:20:29Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• FinEAS: Financial Embedding Analysis of Sentiment [0.0]
  We introduce a new language representation model in finance called Financial Embedding Analysis of Sentiment (FinEAS) In this work, we propose a new model for financial sentiment analysis based on supervised fine-tuned sentence embeddings from a standard BERT model.
  arXiv  Detail & Related papers  (2021-10-31T15:41:56Z)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.