Cross-Model Humor Preference Modeling with Cards Against Humanity
This paper investigates whether one large language model can approximate the humor preferences of another in a controlled Cards Against Humanity-style task. Two models - GPT-4o as Czar and Claude Opus-4.5 as Player - are evaluated on a binary humor-selection task constructed so that success cannot follow from self-preference. A reflected-cell stability procedure isolates 244 hands on which the two models hold deterministic but opposite preferences, partitioned into a 97-hand context pool and a 147-hand held-out test pool. The Player is then evaluated across five graded conditions: default self-preference, generic Czar-modeling instruction, model-identified Czar, prior Czar selections, and...
| Authors | Victor Winter, Farhan Lakhany |
|---|---|
| Primary category | cs.HC |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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