Context Window

Context window refers to the length of text a language model can process at once, a crucial limitation impacting performance on long documents and complex tasks. Current research focuses on extending this window through various techniques, including modifying positional embeddings (like Rotary Position Embeddings) and employing strategies such as attention mechanisms that efficiently handle longer sequences, often without requiring extensive retraining. Overcoming this limitation is vital for advancing applications requiring the processing of extensive textual data, such as long-document question answering, summarization, and complex reasoning tasks.