Summary of Cryptogpt: a 7b Model Rivaling Gpt-4 in the Task Of Analyzing and Classifying Real-time Financial News, by Ying Zhang et al.
CryptoGPT: a 7B model rivaling GPT-4 in the task of analyzing and classifying real-time financial news
by Ying Zhang, Matthieu Petit Guillaume, Aurélien Krauth, Manel Labidi
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Neural and Evolutionary Computing (cs.NE)
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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 A novel approach to refining a limited-resource language model for financial news analysis in real-time is presented in this paper. The CryptoGPT model, competing with GPT-4 in a specific task, is designed for the cryptocurrency market and allows for both classification of financial information and comprehensive analysis. By leveraging semi-automatic annotation and strategic fine-tuning via QLoRA, the authors aim to balance four key needs: protecting data, limiting annotation costs and time, controlling model size, and maintaining better analysis quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a language model called CryptoGPT that can analyze financial news in real-time. This model is special because it’s designed for the cryptocurrency market, which means it can help people understand and make sense of information about different cryptocurrencies. The authors use a technique called semi-automatic annotation to fine-tune their model, which makes it better at doing its job. They also compare their model to other language models like GPT-3.5 and GPT-4. |
Keywords
* Artificial intelligence * Classification * Fine tuning * Gpt * Language model
