Enhancing Zero-Shot Crypto Sentiment Analysis with Fine-Tuned Language Models and Prompt Engineering

Introduction

Blockchain technology has transformed finance through decentralized cryptocurrencies, where social media sentiment plays a pivotal role in shaping market trends. This paper explores advanced sentiment analysis techniques by fine-tuning large language models (LLMs) for zero-shot cryptocurrency sentiment classification, achieving a 40% average performance boost post-optimization.

Key Contributions

1. Fine-Tuning Efficacy

   • Supervised vs. Instruction-Based Tuning: Comparative analysis reveals instruction-based fine-tuning’s superiority for unseen tasks, with larger models achieving 75.16% accuracy.
   • Model Scalability: Larger LLMs benefit significantly from instruction tuning, while smaller models may face reduced generalization due to capacity limits.

2. Instruction Design Impact

   • Short, concise instructions yield 72.38% accuracy, outperforming complex instructions by 12%.
   • Findings underscore the criticality of instruction simplicity in maximizing model performance.

Methodology

• Zero-Shot Learning: Leverages pre-trained knowledge without task-specific training data.
• Prompt Engineering: Optimizes input instructions to guide model responses effectively.

Experimental Results

| Metric | Baseline | Fine-Tuned Model | Improvement |
|----------------------|----------|------------------|-------------|
| Average Accuracy | 35% | 75.16% | +40% |
| Instruction Simplicity Impact | 60% (Complex) | 72.38% (Simple) | +12.38% |

FAQs

Q1: Why is instruction tuning more effective for larger models?
A: Larger models have greater capacity to internalize nuanced instructions, enabling better adaptation to unseen tasks.

Q2: How does prompt engineering enhance sentiment analysis?
A: Well-crafted prompts reduce ambiguity, directing the model to generate contextually accurate sentiment classifications.

Q3: Can small-scale models benefit from instruction tuning?
A: Limited model capacity may lead to overfitting, making simpler architectures less adaptable to diverse instructions.

Conclusion

Fine-tuned LLMs with optimized prompt engineering significantly elevate zero-shot crypto sentiment analysis. Future work could explore hybrid tuning approaches for broader applicability.

👉 Discover advanced crypto analytics tools to leverage sentiment-driven market insights.
👉 Explore cutting-edge LLM applications in decentralized finance.

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