The intent compression paradox: How LLM developers are solving semantic erosion to keep human intent alive
As people delegate more abstract reasoning to large language models (LLMs), the precise meaning and nuance of our words can get lost in endless interpretation as LLMs repeatedly process, summarize, and recirculate these words.
Humans aren’t robots that formalize all of their sentences. Instead, we converse with some unwritten cultural and situational tones. For example, in some cultures, asking “Have you eaten yet?” doesn’t literally mean asking if the person has eaten food, but is another polite way of saying “How are you doing?” An LLM may miss this nuance in an essay, interpreting it literally and giving a wrong output.
If AI Model A interprets “Have you eaten yet?” literally, it then passes that same interpretation to Model B, which then passes it to Model C. At the third layer, the nuance gets lost. The AI models provide a coherent output, but one that’s out of the cultural and...
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