templates/nanoGPT/ideas.json

[
    {
        "Name": "adaptive_block_size",
        "Title": "Adaptive Block Size: Dynamic Context Window Adjustment for Efficient Training",
        "Experiment": "Modify the model to dynamically adjust its block size during training, starting with a smaller block size and gradually increasing it. This could potentially lead to faster initial training and better long-range dependency learning.",
        "Interestingness": 6,
        "Feasibility": 4,
        "Novelty": 4,
        "novel": false
    },
    {
        "Name": "layerwise_learning_rates",
        "Title": "Layer-wise Learning Rate Adaptation: Optimizing Training Dynamics in Transformer Models",
        "Experiment": "Implement layer-wise learning rates, where each transformer layer has its own learning rate. Modify the configure_optimizers function to assign different learning rates to different layers, with deeper layers having lower learning rates. Compare the training dynamics, convergence speed, and final performance with the baseline model.",
        "Interestingness": 4,
        "Feasibility": 6,
        "Novelty": 2,
        "novel": true
    },
    {
        "Name": "comprehensive_initialization",
        "Title": "Comprehensive Study of Initialization Schemes for Transformer Models: Impact on Training Dynamics, Performance, Interpretability, and Robustness",
        "Experiment": [
            "Modify the model initialization function to include options for normal, uniform, Xavier initialization with varying gain parameters.",
            "Train models using each scheme while varying learning rate schedules, optimizers, architectures, tasks, and datasets.",
            "Compare training dynamics, performance, learned weights/attention patterns, and robustness across different initialization schemes.",
            "Visualize and analyze results to provide practical guidelines for choosing appropriate initialization schemes."
        ],
        "Interestingness": 9,
        "Feasibility": 7,
        "Novelty": 8,
        "novel": false
    },
    {
        "Name": "refined_augmented_preprocessing",
        "Title": "Interpreting the Impact of Alternative Preprocessing Techniques and Data Augmentation on Transformer Model Performance",
        "Experiment": [
            "Extend previous experiment to include interpretability analysis alongside attention patterns using techniques like LIME, SHAP, or integrated gradients.",
            "Conduct a comprehensive ablation study to quantify the individual contributions of preprocessing methods and data augmentation strategies.",
            "Publicly release detailed implementation notes, data processing scripts, and well-commented code for reproducibility and further research."
        ],
        "Interestingness": 9,
        "Feasibility": 8,
        "Novelty": 7,
        "novel": true
    }
]