Training and Validation

Chapter OverviewThis chapter provides a comprehensive understanding of the training and validation process of GPT-2, focusing on dataset preparation, model optimization, and evaluation metrics. You will learn how GPT-2 is trained using unsupervised learning, the importance of autoregressive language modeling, and how to validate the model's performance using perplexity and other evaluation techniques. The chapter also covers fine-tuning strategies and best practices for improving generalization.Training GPT-2: An OverviewGPT-2 follows an unsupervised learning approach where it learns to predict the next token in a sequence based on previous tokens. The training process involves the following key steps:Dataset Preparation: Large-scale text datasets (e.g., WebText) are tokenized and formatted for autoregressive training.Tokenization: The dataset is converted into tokenized sequences using Byte Pair Encoding (BPE).Model Initialization: The transformer-based architecture is initialized with random weights.Autoregressive Learning: The model learns to predict the next token given the preceding tokens.Validation Process: Evaluating Model PerformancePerplexity as an Evaluation Metric: Lower perplexity = better model performance. A low perplexity score means the model is better at predicting text sequences.While perplexity provides a numerical score, human evaluation is essential to assess coherence, fluency, and factual accuracy.🔹 Common human evaluation techniques:Sampling Model Outputs: Checking if generated text is fluent and contextually relevant.Prompt Testing: Testing the model with diverse prompts to see how it generalizes.Bias Detection: Analyzing if the model produces biased or harmful outputs.Fine-Tuning GPT-2 for Specific TasksFine-tuning allows GPT-2 to specialize in specific domains (e.g., medical, legal, financial text). The steps include:Selecting a Domain-Specific Dataset (e.g., medical journals for a healthcare chatbot).Continuing Training on Pretrained GPT-2 Weights for improved domain adaptation.Using Transfer Learning Techniques to adjust GPT-2 for classification or summarization tasks.Fine-tuning typically requires:Lower learning rates to prevent catastrophic forgetting.Custom loss functions if targeting classification or other structured outputs.Challenges in GPT-2 Training & ValidationTraining large transformer models comes with several challenges:🔹 Compute Costs:GPT-2 training requires high-performance GPUs/TPUs.Distributed training techniques like Data Parallelism (DDP) help scale training.🔹 Overfitting & Generalization:Large models can memorize training data, reducing generalization.Regularization techniques like dropout and data augmentation help mitigate overfitting.🔹 Ethical Concerns:GPT-2 can generate biased or misleading content.Bias mitigation strategies (e.g., dataset filtering) are necessary.