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.

翻译：社交媒体的快速发展催生了海量用户生成内容，尤其在加密货币等细分领域。本研究的核心目标是开发鲁棒的分类模型，以准确地将与加密货币相关的社交媒体帖子归类至预定义类别，包括但不限于客观、积极、消极等。此外，研究要求参与者从一组帖子中识别出针对特定问题的最相关答案。通过利用先进的大型语言模型，本研究旨在提升对加密货币讨论的理解与筛选能力，从而在这一波动性较强的领域促进更明智的决策。我们采用基于提示的技术来解决Reddit帖子和Twitter帖子的分类任务。同时，我们结合64样本提示技术在GPT-4-Turbo模型上判断答案与问题的相关性。