The author argues that large language models (LLMs) should be designed to access external information rather than solely relying on memorization. This approach would allow LLMs to provide more accurate and up-to-date answers, especially for questions outside their training data. By teaching LLMs to 'look things up,' developers can improve their reliability and utility. AI
IMPACT Suggests a shift in LLM development towards external information retrieval for improved accuracy and relevance.
RANK_REASON Opinion piece discussing a conceptual approach to improving LLM functionality.
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