Deep Dive into Long Context

This episode explores the advancements and challenges in long-context large language models (LLMs). The interview begins by defining tokens and their role in LLMs, highlighting the differences between how humans and LLMs process text. Against this backdrop, the discussion pivots to context windows, differentiating between in-weight (pre-training) and in-context memory, and explaining how Retrieval Augmented Generation (RAG) systems supplement in-context memory. More significantly, the conversation delves into the limitations of scaling context windows beyond 1-2 million tokens, citing cost and architectural constraints as primary factors. For instance, while a 10-million-token model showed promising results, its high cost prevented widespread deployment. As the discussion concludes, the guest predicts future improvements in long-context capabilities, focusing on enhanced quality, reduced cost, and the integration of long context into other AI research areas, ultimately leading to transformative applications in coding and beyond. This means a future where LLMs can handle massive codebases, enabling superhuman coding capabilities.