huggingface--peft/method_comparison/text_generation_benchmark/configs/prompts.json

{
  "short": [
    "Explain quantum computing in one paragraph.",
    "Write a haiku about machine learning.",
    "What's the difference between supervised and unsupervised learning?",
    "Define parameter-efficient fine-tuning in one sentence.",
    "List three applications of natural language processing."
  ],
  "medium": [
    "Explain the concept of low-rank adaptation (LoRA) for large language models. Include its benefits and limitations.",
    "Compare and contrast prompt tuning and prefix tuning approaches for adapting large language models.",
    "What are the key differences between full fine-tuning and parameter-efficient methods? Explain with examples.",
    "Describe the process of quantization for neural networks and how it affects model size and inference speed.",
    "Explain how sparse expert models like Mixture of Experts work and their advantages over dense models."
  ],
  "long": [
    "Analyze the evolution of parameter-efficient fine-tuning methods from 2020 to present. Include a detailed comparison of at least five different approaches, their theoretical foundations, and practical implications for deploying large language models.",
    "Provide a comprehensive tutorial on implementing LoRA for a transformer-based language model. Include code examples, hyperparameter selection guidance, and best practices for training and deployment.",
    "Compare the computational efficiency, parameter count, and performance characteristics of different PEFT methods (LoRA, Prefix Tuning, Prompt Tuning, IA3, AdaLoRA) across various downstream tasks. Include a discussion of when each method is most appropriate.",
    "Explain the mathematical foundations of various parameter-efficient fine-tuning techniques. Discuss how each technique modifies the original neural network architecture and the optimization challenges involved.",
    "Discuss the ethical implications of parameter-efficient fine-tuning methods in democratizing access to large language models. Include considerations about computational resources, environmental impact, and accessibility for researchers in resource-constrained settings."
  ]
}