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

2024-11-12

arXiv.org

AI Summary

This work addresses two core challenges in cryptocurrency-related social media content (Reddit/Twitter): semantic classification and question-answer relevance retrieval. We propose a lightweight, large language model–based intelligent analysis framework. Methodologically, we introduce a novel 64-shot decoupled prompting strategy: separating and optimizing sentiment/intent classification (objective/positive/negative) from cross-post relevance judgment, integrated with task-specific templated prompts, multi-round relevance scoring, and in-context learning—enabling zero-shot cross-platform transfer under few-shot settings. Experiments demonstrate strong performance: 89.3% F1 score for classification and 92.7% relevance accuracy for SmartQnA, significantly outperforming fine-tuned baselines. Critically, the framework requires no parameter fine-tuning, achieving high accuracy, robust generalization across platforms and domains, and practical deployability in real-world cryptocurrency communities. It establishes a new paradigm for low-resource content understanding in highly volatile domains.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Community question answering
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• Classify cryptocurrency social media posts into predefined categories.
• Identify relevant answers from posts for specific cryptocurrency questions.
• Enhance cryptocurrency discourse understanding using advanced LLMs.

Innovation

• Prompt-based technique for crypto post classification
• 64-shot learning with GPT-4-Turbo for relevance detection
• Advanced LLMs to filter and analyze cryptocurrency discourse