Yes, there are several architectural improvements in GPT-3.5 that are known or can be inferred from published research papers and other sources. Here are some examples:
Adaptive Prompt-based Training: GPT-3.5 uses a novel training method called adaptive prompt-based training, which involves using a set of prompts tailored to each specific task to improve the model’s performance on that task.
Sparse Transformer: GPT-3.5 uses a sparse attention mechanism called the Sparse Transformer, which reduces the computational cost of attention by only attending to a subset of the input tokens.
Self-Training with Denoising: GPT-3.5 uses a self-training approach with denoising to improve the model’s robustness to noise and errors in the input data.
Regularization Techniques: GPT-3.5 uses various regularization techniques, such as dropout, weight decay, and layer normalization, to prevent overfitting and improve generalization performance.
Pre-Training on Diverse Data Sources: GPT-3.5 has been pre-trained on a diverse set of data sources, including books, articles, and web pages, which allows it to learn from a wide range of linguistic and topical domains.
Fine-tuning on conversational data: One of the most effective ways to improve LLaMA’s performance on conversational tasks is to fine-tune it on conversational data. This involves training the model on a large dataset of conversational text, such as chat logs or social media conversations, to help it learn the nuances of natural language interactions.
Ensembling with other models: Another approach is to ensemble LLaMA with other models that are designed specifically for conversational tasks. This can help to leverage the strengths of each model and improve overall performance.
Improving the training data: LLaMA has been trained on a large training set of 1.4T tokens, but it’s possible that the quality of the data could be improved. For example, you might consider incorporating more diverse or higher-quality data sources, or using techniques like data augmentation to increase the variety of training examples.
Tuning model hyperparameters: Model hyperparameters, such as learning rate, batch size, and optimizer, can have a significant impact on model performance. Tuning these hyperparameters on a validation set can help to find the optimal settings for LLaMA on conversational tasks.
Flash Attention, 32K tokens https://www.reddit.com/r/MachineLearning/comments/11tmpc5/d_pytorch_20_native_flash_attention_32k_context/