CryptoGPT: a 7B model rivaling GPT-4 in the task of analyzing and classifying real-time financial news
This work addresses the need for efficient, cost-effective, and privacy-preserving financial analysis tools in the cryptocurrency industry, though it is incremental as it builds on existing LLMs with targeted improvements.
The authors tackled the problem of analyzing and classifying real-time financial news for the cryptocurrency market with limited resources, achieving a 7B model that rivals GPT-4 in this specific task through automatic annotation and strategic fine-tuning.
CryptoGPT: a 7B model competing with GPT-4 in a specific task -- The Impact of Automatic Annotation and Strategic Fine-Tuning via QLoRAIn this article, we present a method aimed at refining a dedicated LLM of reasonable quality with limited resources in an industrial setting via CryptoGPT. It is an LLM designed for financial news analysis for the cryptocurrency market in real-time. This project was launched in an industrial context. This model allows not only for the classification of financial information but also for providing comprehensive analysis. We refined different LLMs of the same size such as Mistral-7B and LLama-7B using semi-automatic annotation and compared them with various LLMs such as GPT-3.5 and GPT-4. Our goal is to find a balance among several needs: 1. Protecting data (by avoiding their transfer to external servers), 2. Limiting annotation cost and time, 3. Controlling the model's size (to manage deployment costs), and 4. Maintaining better analysis quality.