Why AI language models choke on too much text

Large language models (LLMs) face significant challenges when processing extensive text due to their architecture and the computational costs associated with attention mechanisms. Initially, models like OpenAI's ChatGPT had a context window of 8,192 tokens, limiting their ability to retain information from longer texts. Recent advancements have increased this capacity, with models like GPT-4o handling 128,000 tokens and Google's Gemini 1.5 Pro managing up to 2 million tokens. However, these improvements still fall short of human cognitive abilities, as humans can process vast amounts of information over time. Current systems often rely on retrieval-augmented generation (RAG) to manage large datasets, but these systems can struggle with complex queries and may retrieve irrelevant documents. The attention mechanism, crucial for LLMs, becomes increasingly inefficient as the context grows, leading to quadratic increases in computational requirements. Researchers are exploring various methods to enhance attention efficiency, including FlashAttention and ring attention, which aim to optimize performance on modern GPUs. Despite these efforts, achieving human-level understanding and memory in AI remains a significant hurdle.

- LLMs have improved context windows but still struggle with extensive text.

- Current systems often use retrieval-augmented generation to manage large datasets.

- The attention mechanism's computational cost grows quadratically with input size.

- Research is ongoing to enhance attention efficiency in LLMs.

- Achieving human-level cognitive abilities in AI remains a significant challenge.