Summary of Pre-trained Large Language Models For Financial Sentiment Analysis, by Wei Luo et al.
Pre-trained Large Language Models for Financial Sentiment Analysis
by Wei Luo, Dihong Gong
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to financial sentiment analysis, specifically focusing on classifying financial news titles. The task is challenging due to limited training data, but the authors overcome this hurdle by adapting pre-trained large language models (LLMs) and fine-tuning them using supervised learning techniques. By leveraging the LLMs’ ability to understand text and domain-specific expertise, the authors achieve state-of-the-art performance even with a relatively small model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Financial sentiment analysis is important for investors and analysts to make informed decisions. This paper makes it easier by developing an AI-powered approach that can classify financial news titles into positive, negative, or neutral sentiments. The method uses pre-trained language models that are fine-tuned for the task, requiring very little training data. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
