Latest Trends in Large Language Models: Architectures, Applications, and Tokenization

Recent advancements in Large Language Models (LLMs) showcase diverse approaches, from Mixture of Experts to innovative finetuning methods and continual pretraining strategies. The debate around alignment techniques like DPO and PPO continues, alongside exploration of datasets such as FineWeb. Models like Llama 3 and Phi-4 demonstrate scaling capabilities and the use of synthetic data. Building successful applications requires careful consideration, avoiding common pitfalls such as over-reliance on generative AI when simpler solutions exist, and recognizing that user experience is often the key differentiator. Human evaluation remains crucial for refining AI judges and improving overall product quality. Additionally, an educational, from-scratch implementation of the Byte Pair Encoding (BPE) tokenization algorithm, commonly used in models like GPT-2 and Llama 3 is available.