Business Context
TechCorp, a leading e-commerce platform, is exploring advanced NLP techniques to enhance its customer support system. The team is evaluating whether to implement a zero-shot prompting approach using GPT-4 or to fine-tune a smaller model like LLaMA-3 for classifying customer inquiries into predefined categories.
Data Characteristics
- Volume: 100,000 customer inquiries
- Text Length: Average of 50-200 words
- Language: English
- Label Distribution: 30% Product Inquiry, 25% Order Status, 20% Returns, 15% Technical Support, 10% General Questions
Success Criteria
- Achieve at least 85% accuracy across all categories.
- Maintain inference latency under 200ms for real-time response.
Constraints
- Zero-shot prompting must use the existing model without additional training.
- Fine-tuning must limit model size to under 1 billion parameters to fit within deployment constraints.
Requirements
- Analyze strengths and weaknesses of zero-shot prompting with GPT-4.
- Evaluate the benefits of fine-tuning LLaMA-3 for the specific task.
- Provide recommendations based on performance metrics, deployment considerations, and scalability.
- Discuss potential impacts on model interpretability and user experience.
