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Storing knowledge directly in LLM parameters offers a flexible, efficient alternative to in-context learning, bypassing context length limitations and repeated encoding costs.
Context construction can be dynamically tailored to specific queries, leading to substantial improvements in multi-hop question answering and summarization tasks.