Long Context Language

Long-context language models (LLMs) aim to process significantly longer input sequences than traditional models, enabling more comprehensive understanding and generation of text. Current research focuses on developing more effective training methods, evaluating model performance across diverse and realistic tasks beyond simple retrieval benchmarks, and improving the efficiency of attention mechanisms to handle extremely long contexts. This field is crucial for advancing natural language processing capabilities in applications requiring extensive contextual information, such as complex question answering, document summarization, and code generation.