When language models answer open-ended questions, they implicitly make decisions that shape their answers. However, the models do not show these decisions to the user, so users cannot contextualize answers: they cannot see the assumptions an answer rests on or the alternatives to it. Drawing on multiverse analysis from statistics, we introduce the conceptual multiverse, a representation in which language models express answers as the result of intermediate conceptual decisions. We build a system that generates such decisions explicitly, organizes them into a space of answers that users can see and change, and checks them against the norms of a specific domain. In a user study across three domains - philosophy, AI alignment annotation, and poetry - participants say that our system helps them see the decisions leading to an answer, explore the space of answers, and understand the decisions structuring this space.

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