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.

我们提供了一个独特的多模态偏好数据集，用于创意任务，在过去的八年中通过众包整理了超过 250 万个标注为《纽约客》周刊漫画字幕比赛的超过 2.2 亿个人类评分。通过结合 GPT4 和人类判断，建立了基于排名的评估策略，我们对模型生成的字幕质量提出了新的评估标准。实验证明了当前的 Fine-Tuning 方法在创意任务中的局限性，并且我们展示了即使是 GPT4 和 Claude 这样的最先进模型在生成幽默字幕方面也不及顶级人类参赛者。经过这项大规模的数据收集工作，我们将整个偏好数据集释放给研究界，促进 AI 幽默生成和评估的进一步发展。