用提示工程让大模型读懂加密货币社交帖子，自动分类与找答案
CryptoLLM: Unleashing the Power of Prompted LLMs for SmartQnA and Classification of Crypto Posts
- 用提示词设计让大模型理解加密币帖内容
- 在Reddit和Twitter数据上实现高精度分类与问答匹配
- 适合想快速分析加密社区情绪和信息的从业者
社交媒体的快速发展带来了大量用户生成内容，尤其在加密货币等细分领域。本研究旨在构建稳健的分类模型，准确将加密货币相关社交帖子归类为客观、正面、负面等预定义类别，并从一组帖子中识别出针对特定问题最相关的回答。通过使用先进的大语言模型，提升对加密货币舆论的理解与筛选能力，助力该高波动性领域做出更明智决策。研究采用提示工程方法处理Reddit和Twitter帖子的分类任务，同时结合64张示例提示（64-shot）与GPT-4-Turbo模型，判断回答是否与问题相关。
原文摘要 · Abstract (English)
The rapid growth of social media has resulted in an large volume of user-generated content, particularly in niche domains such as cryptocurrency. This task focuses on developing robust classification models to accurately categorize cryptocurrency-related social media posts into predefined classes, including but not limited to objective, positive, negative, etc. Additionally, the task requires participants to identify the most relevant answers from a set of posts in response to specific questions. By leveraging advanced LLMs, this research aims to enhance the understanding and filtering of cryptocurrency discourse, thereby facilitating more informed decision-making in this volatile sector. We have used a prompt-based technique to solve the classification task for reddit posts and twitter posts. Also, we have used 64-shot technique along with prompts on GPT-4-Turbo model to determine whether a answer is relevant to a question or not.
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