  We present a novel multimodal preference dataset for creative tasks,
consisting of over 250 million human ratings on more than 2.2 million captions,
collected through crowdsourcing rating data for The New Yorker's weekly cartoon
caption contest over the past eight years. This unique dataset supports the
development and evaluation of multimodal large language models and
preference-based fine-tuning algorithms for humorous caption generation. We
propose novel benchmarks for judging the quality of model-generated captions,
utilizing both GPT4 and human judgments to establish ranking-based evaluation
strategies. Our experimental results highlight the limitations of current
fine-tuning methods, such as RLHF and DPO, when applied to creative tasks.
Furthermore, we demonstrate that even state-of-the-art models like GPT4 and
Claude currently underperform top human contestants in generating humorous
captions. As we conclude this extensive data collection effort, we release the
entire preference dataset to the research community, fostering further
advancements in AI humor generation and evaluation.
