Input Context

Input context, the information provided to a language model before a task is performed, is crucial for model performance and faithfulness. Current research focuses on improving how models utilize this context, addressing challenges like limited context windows, attention bias towards beginning and end of input, and the difficulty of processing long or complex information. This involves developing techniques like attention steering, memory-augmented retrieval, and context compression to enhance both efficiency and accuracy, with a particular emphasis on mitigating "lost-in-the-middle" effects and improving faithfulness in generated outputs. These advancements are significant for improving the reliability and scalability of large language models across various applications.