The author also mentions his upcoming book, "Build a Large Language Model (from Scratch)", which aims to provide a comprehensive guide to coding a large language model using PyTorch. The book covers everything from coding the data input pipeline to implementing attention mechanisms from scratch and pretraining and finetuning the LLM. The author concludes by expressing his hope for more well-written papers and studies in the coming year.
Key takeaways:
- The author highlights 10 significant papers in the field of machine learning and AI research from 2023, with a focus on large language models (LLMs).
- Some of the noteworthy papers include "Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling", "Llama 2: Open Foundation and Fine-Tuned Chat Models", and "QLoRA: Efficient Finetuning of Quantized LLMs".
- The author also discusses the importance of high-quality data for finetuning models, the potential of Mixture of Experts (MoE) models, and the competitive performance of convolutional neural networks (CNNs) when given access to large datasets.
- Looking ahead, the author anticipates an increase in the adoption of Direct Preference Optimization (DPO) models and the prevalence of text-to-video models in the upcoming year.