दीर्घ-संदर्भ मॉडलिंग

The Transformer’s standard self-attention computes interactions across token positions, and its attention computation grows quadratically with sequence length. FlashAttention is an exact, IO-aware attention algorithm that uses tiling to reduce reads and writes between GPU memory levels; it does not remove the underlying attention relationships. Rotary position embeddings (RoPE) encode token positions. Position Interpolation is a paper-specific method that rescales positions to extend context in RoPE-based pretrained models and is evaluated on tasks such as retrieval and long-document summarization. The Lost in the Middle study finds position-sensitive performance on long-context QA and retrieval tasks. RULER extends simple needle retrieval with multi-hop tracing and aggregation. Prompt Cache studies reusing attention state for reusable prompt segments. These are distinct methods with different assumptions; longer context limits do not ensure reliable use of all supplied information.