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.

本研究针对社交媒体中加密货币相关内容的快速增长，开发了强大的分类模型，以准确将相关帖子归类。研究通过创新的提示技术和64-shot方法，利用GPT-4-Turbo模型来识别与具体问题相关的答案，旨在提升对加密货币话语的理解和筛选，从而促进更明智的决策。

CryptoLLM：释放提示式大型语言模型在智能问答和加密帖子分类中的力量