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Know the Limits of Familiarity and Unfamiliarity in LLMs
Language models struggle with unfamiliar concepts, relying on past patterns which can lead to incorrect outputs. They cannot discern between correct and incorrect information if it falls outside their training data. This limitation emphasizes the necessity of manual data annotation. Conversely, when models are overly familiar with certain queries, they risk applying learned patterns without true understanding, as demonstrated by common logic puzzles. Early LLMs often misinterpreted such queries, responding based on memorized patterns rather than comprehension. While improvements have been made through targeted updates, they continue to operate largely on a reactive basis rather than through genuine understanding.