Timothy B. Lee (Understanding AI) explains Why large language models struggle with long contexts.
To achieve human-level intelligence, AI systems must process and understand vast amounts of information. Retrieval-Augmented Generation (RAG) systems enhance LLMs by retrieving relevant documents to include in the context window, improving response accuracy. However, the effectiveness of RAG systems depends on accurately selecting relevant documents; failures in retrieval can lead to incorrect AI responses. Tasks such as legal document review, factory surveillance analysis, and medical research require AI to handle data exceeding current context window capacities.
Future AI systems should build upon previous interactions and data, similar to human learning, rather than starting from scratch each time. Transformer-based LLMs face efficiency challenges as context windows expand due to the computational demands of attention mechanisms, which allow models to consider previous tokens when generating new ones but scale poorly with increasing context sizes. Researchers are actively seeking solutions to enhance LLM efficiency and scalability to manage larger contexts effectively.
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