用微调的Mistral模型结合检索生成，多层级分析加密货币新闻。
Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model
- 用微调的Mistral 7B模型+检索增强生成，实现多层级新闻分析。
- 生成图文摘要与情感分数，支持知识图谱消减幻觉问题。
- 适合金融量化、加密资产监控等场景的智能分析需求。
本文提出一种基于检索增强生成（RAG）和微调的Mistral 7B大语言模型的多层级多任务加密货币新闻分析方法。第一层分析中，模型生成图文摘要、情感分数及摘要的JSON表示；更高层级通过层次化堆叠，将图结构摘要、文本摘要及摘要的摘要整合为综合报告。图文摘要提供互补视角，提升分析深度。模型采用4比特量化与PEFT/LoRA方法进行微调，将加密货币新闻表示为知识图谱，有效缓解大模型幻觉问题。实验表明，该方法可实现信息丰富且可量化的定性与定量分析，为加密资产研究提供关键洞见。
原文摘要 · Abstract (English)
In the paper, we consider multilevel multitask analysis of cryptocurrency news using a fine-tuned Mistral 7B large language model with retrieval-augmented generation (RAG). On the first level of analytics, the fine-tuned model generates graph and text summaries with sentiment scores as well as JSON representations of summaries. Higher levels perform hierarchical stacking that consolidates sets of graph-based and text-based summaries as well as summaries of summaries into comprehensive reports. The combination of graph and text summaries provides complementary views of cryptocurrency news. The model is fine-tuned with 4-bit quantization using the PEFT/LoRA approach. The representation of cryptocurrency news as knowledge graph can essentially eliminate problems with large language model hallucinations. The obtained results demonstrate that the use of fine-tuned Mistral 7B LLM models for multilevel cryptocurrency news analysis can conduct informative qualitative and quantitative analytics, providing important insights.
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