How do LLMs handle long-context limitations?

LLMs use attention optimizations, chunking, and memory mechanisms to handle long contexts.

Standard transformers struggle with quadratic attention cost. Long-context solutions include sparse attention, sliding window attention, recurrence mechanisms, and external memory systems. Some models also use retrieval augmentation to avoid processing full context.