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

arXiv: https://arxiv.org/abs/2411.07917 · HTML: https://mlsanity.com/paper/2411.07917/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 60 references that are in the map.
- First posted on arXiv 12 Nov 2024.

## Builds on (most cited first, 27 of 27)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Diverse Few-Shot Text Classification with Multiple Metrics](https://mlsanity.com/paper/1805.07513/) (2018)
- [Few-shot Text Classification with Distributional Signatures](https://mlsanity.com/paper/1908.06039/) (2019)
- [FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering](https://mlsanity.com/paper/2308.12060/) (2023)
- [How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?](https://mlsanity.com/paper/2306.01248/) (2023)
- [PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models](https://mlsanity.com/paper/2204.01172/) (2022)
- [Few shot chain-of-thought driven reasoning to prompt LLMs for open ended medical question answering](https://mlsanity.com/paper/2403.04890/) (2024)
- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [A Novel Multi-Stage Prompting Approach for Language Agnostic MCQ Generation using GPT](https://mlsanity.com/paper/2401.07098/) (2024)
- [Exploring the Capabilities of Prompted Large Language Models in Educational and Assessment Applications](https://mlsanity.com/paper/2405.11579/) (2024)
- [How Ready Are Generative Pre-trained Large Language Models for Explaining Bengali Grammatical Errors?](https://mlsanity.com/paper/2406.00039/) (2024)
- [Generative AI and Its Impact on Personalized Intelligent Tutoring Systems](https://mlsanity.com/paper/2410.10650/) (2024)
- [Fact-based Court Judgment Prediction](https://mlsanity.com/paper/2311.13350/) (2023)
- [Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models](https://mlsanity.com/paper/2410.10542/) (2024)
- [Nonet at SemEval-2023 Task 6: Methodologies for Legal Evaluation](https://mlsanity.com/paper/2310.11049/) (2023)
- [Harnessing the Power of Prompt-based Techniques for Generating School-Level Questions using Large Language Models](https://mlsanity.com/paper/2312.01032/) (2023)
- [How Effective is GPT-4 Turbo in Generating School-Level Questions from Textbooks Based on Bloom's Revised Taxonomy?](https://mlsanity.com/paper/2406.15211/) (2024)
- [Questioning Biases in Case Judgment Summaries: Legal Datasets or Large Language Models?](https://mlsanity.com/paper/2312.00554/) (2023)
- [Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B](https://mlsanity.com/paper/2409.19027/) (2024)
- [The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models](https://mlsanity.com/paper/2410.09576/) (2024)
- [Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study](https://mlsanity.com/paper/2401.07310/) (2024)
- [Artificial Intelligence (AI) in Legal Data Mining](https://mlsanity.com/paper/2405.14707/) (2024)
- [MIRROR: A Novel Approach for the Automated Evaluation of Open-Ended Question Generation](https://mlsanity.com/paper/2410.12893/) (2024)
- [Prompted Zero-Shot Multi-label Classification of Factual Incorrectness in Machine-Generated Summaries](https://mlsanity.com/paper/2312.01087/) (2023)
- [Multi-Label Classification of COVID-Tweets Using Large Language Models](https://mlsanity.com/paper/2312.10748/) (2023)

## Abstract (arXiv)

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.