A dataset of rated conceptual arguments
Large language models have improved rapidly on tasks with verifiable answers, such as mathematics and programming. Much less is known about their ability to reason about what we call conceptual questions: questions for which no ground truth is realistically accessible and no widely accepted resolution methodology exists, but on which progress can still be made by debating arguments. Most philosophical questions are of this kind, as are central components of questions in AI safety, decision theory, and social choice. Our approach is based on the view that while bottom-line conclusions on such questions are hard to evaluate, individual contextualized arguments can be evaluated far more reli...
| Authors | Emery Cooper, Caspar Oesterheld, Linh Chi Nguyen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 July 2026 |
| Last updated at source | 29 July 2026 |
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