The author argues that the perceived flaws in Large Language Models (LLMs) are not inherent to the AI itself, but rather a direct result of the poor guidance and direction provided by their human operators. This perspective suggests that the responsibility for an LLM's output and performance lies squarely with the individuals controlling and training them. AI
IMPACT This perspective challenges the common discourse around LLM limitations, shifting focus to human oversight and training practices.
RANK_REASON Opinion piece from a named individual on the nature of LLM flaws.
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