Summary of Large Language Model Adaptation For Financial Sentiment Analysis, by Pau Rodriguez Inserte et al.
Large Language Model Adaptation for Financial Sentiment Analysis
by Pau Rodriguez Inserte, Mariam Nakhlé, Raheel Qader, Gaetan Caillaut, Jingshu Liu
First submitted to arxiv on: 26 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 The proposed study focuses on adapting large language models (LLMs) for natural language processing (NLP) tasks in finance, specifically financial sentiment analysis. Generalist LLMs struggle with financial texts due to complexity and domain-specific terminology. The researchers present two foundation models with fewer than 1.5 billion parameters, fine-tuning them using various strategies on financial documents and instructions. The results show that these smaller models can achieve comparable performance to larger ones while being more efficient in terms of parameters and data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps improve financial insights by adapting language models for the finance domain. It’s like teaching a computer to understand financial reports better, making it easier to make smart investment decisions. The researchers used special training methods to help smaller language models be just as good at understanding financial texts as bigger ones. This could make a big difference in how efficiently computers can analyze financial data. |
Keywords
* Artificial intelligence * Fine tuning * Natural language processing * Nlp
