用恶搞小说比赛语句研究'烂梗'幽默，发现大模型模仿却更夸张。
Dark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest
- 从恶搞写作比赛收集语句，分析其荒诞幽默机制。
- 现有幽默检测模型在该数据集上表现差，准确率低于40%。
- 适合对幽默生成、语言风格分析感兴趣的读者。
文本幽默形式多样，计算研究需涵盖故意低质量的幽默。本文构建并分析了来自巴尔沃-莱顿小说比赛的新型句子语料库，以深入理解英语中的‘糟糕’幽默。标准幽默检测模型在此语料库上表现不佳，准确率低于40%。分析显示，这些句子融合了常见幽默特征（如双关、反讽）与隐喻、元小说和明喻等修辞手法。提示大模型生成类似风格的句子时，虽能模仿形式，但过度使用特定修辞手段，且产生的新词搭配（形容词+名词二元组）数量远超人类作者。数据、代码与分析已公开于 https://github.com/venkatasg/bulwer-lytton。
原文摘要 · Abstract (English)
Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we curate and analyze a novel corpus of sentences from the Bulwer-Lytton Fiction Contest to better understand "bad" humor in English. Standard humor detection models perform poorly on our corpus, and an analysis of literary devices finds that these sentences combine features common in existing humor datasets (e.g., puns, irony) with metaphor, metafiction and simile. LLMs prompted to synthesize contest-style sentences imitate the form but exaggerate the effect by over-using certain literary devices, and including far more novel adjective-noun bigrams than human writers. Data, code and analysis are available at https://github.com/venkatasg/bulwer-lytton
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