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LLMs can predict humor preferences of other models, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores whether one large language model can predict the humor preferences of another using a Cards Against Humanity-style task. The study pitted GPT-4o against Claude Opus 4.5, finding that while simple instructions to mimic the other model yielded minimal improvement, providing behavioral evidence of the other model's choices, especially with rationales, significantly boosted accuracy. This suggests a form of theory-of-mind-like behavior where models can adapt their responses based on observed preferences, rather than internalizing them. AI

IMPACT Demonstrates LLMs can exhibit theory-of-mind-like behavior, potentially improving agent collaboration and personalized AI interactions.

RANK_REASON The cluster contains a research paper detailing an experiment on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs can predict humor preferences of other models, study finds

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The cluster contains a research paper detailing an experiment on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Victor Winter, Farhan Lakhany ·

    Cross-Model Humor Preference Modeling with Cards Against Humanity

    arXiv:2608.07481v1 Announce Type: cross Abstract: 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…

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