CryptoLLM: Unleashing the Power of Prompted LLMs for SmartQnA and Classification of Crypto Posts

TL;DR

This paper introduces CryptoLLM, a prompt-based approach using GPT-4-Turbo with 64-shot learning to classify and identify relevant answers in cryptocurrency social media posts, improving understanding in this niche domain.

Contribution

It presents a novel prompt-based methodology leveraging advanced LLMs for classifying crypto posts and identifying relevant answers, tailored for social media content.

Findings

Effective classification of crypto posts achieved

High accuracy in answer relevance detection

Demonstrated utility of prompt-based 64-shot learning

Abstract

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.