4 posts are grouped under this topic.
While there is a close relationship between data volume and model performance, unconditional data accumulation does not necessarily guarantee optimal intelligence. Traditionally, acquiring more training data was consider
Recent advancements in LLMs have seen context windows explode—from GPT-4 Turbo (128K) to Gemini 1.5 Pro (1M+)—making long-context processing a core competitive advantage [S2439]. However, amidst this technological surge,
When working with Large Language Models (LLMs) to handle long documents, we frequently encounter situations where increasing the prompt length leads to an explosive rise in VRAM usage, eventually causing the system to st
At their core, Large Language Models (LLMs) are auto-regressive models that predict the next token based on preceding ones. This inherent characteristic leads to a structural problem: every time a new token is generated,