[
    {
        "question": "What is LoRA?",
        "answer": "LoRA is a method that freezes pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture to reduce the number of trainable parameters."
    },
    {
        "question": "What are the advantages of LoRA?",
        "answer": "The advantages of LoRA include reduced number of trainable parameters, lower GPU memory requirement, higher training throughput, no additional inference latency, and the ability to switch tasks efficiently."
    },
    {
        "question": "How does LoRA compare to fine-tuning in terms of model quality?",
        "answer": "LoRA performs on-par or better than fine-tuning in model quality on various language models, despite having fewer trainable parameters and higher training throughput."
    },
    {
        "question": "Which weight matrices in the Transformer architecture should be adapted with LoRA?",
        "answer": "LoRA should be applied to the weight matrices in the self-attention module, specifically Wq and Wv, for optimal performance."
    },
    {
        "question": "What is the optimal rank for LoRA?",
        "answer": "A low rank, such as 1 or 2, is sufficient for LoRA to achieve competitive performance on downstream tasks."
    },
    {
        "question": "What is the advantage of few-shot learning?",
        "answer": "Few-shot learning is advantageous when we only have a handful of training samples."
    },
    {
        "question": "What is the difference between adapter layers and LoRA?",
        "answer": "Adapter layers are computed in addition to the base model, introducing additional latency, while LoRA is added in a parallel manner."
    },
    {
        "question": "What is the GLUE Benchmark?",
        "answer": "The GLUE Benchmark is a collection of natural language understanding tasks used to evaluate NLU models."
    },
    {
        "question": "What is the purpose of the E2E NLG Challenge dataset?",
        "answer": "The E2E NLG Challenge dataset is used for training end-to-end, data-driven natural language generation systems."
    },
    {
        "question": "What is the amplification factor for task-specific directions in LoRA?",
        "answer": "The amplification factor for task-specific directions in LoRA is around 20."
    }
]