原文依据： 「In this work, we investigate whether the ability of Large Language Models (LLMs) to explain humour depends on the particular humour form.」（出自：Abstract，第1页）
原文依据： 「…where humour arises from the interpretation of the punning word (i.e., “dyed”) with a phonetically similar word with different meaning (i.e., “died”) in the case of heterographic puns…」（出自：Introduction，第1页）
原文依据： 「On the other hand, much of the humour encountered online… is based on contemporary topical knowledge of evolving pop-culture phenomena and news events, where a full appreciation of the humour relies on potentially esoteric knowledge, rather than common-sense…」（出自：Introduction，第1页）
原文依据： 「To assess the quality of joke explanations generated by the models, we propose a scoring rubric with two core criteria, accuracy and completeness, each evaluated on a 6-point scale (0-5).」（出自：4.3 Evaluation Criteria，第5页）
原文依据： 「When comparing model performance, GPT-4o consistently outperforms all others, demonstrating the highest accuracy and completeness scores across joke types.」（出自：5 Human Evaluation，第5页）
A. 在准确性（Accuracy）和完整性（Completeness）两个标准上得分都达到4分或以上。✅
B. 解释的长度超过100个单词。✅
C. 至少在一个评估标准上获得满分5分。✅
D. 由LLM-Judge判定为「通过」。✅
正确答案： A
原文依据： 「…we present a binary categorisation of explanations, treating scores of 4 or above on both criteria as being “good” quality explanations…」（出自：5.1 Explanation Success Rate，第6页）
原文依据： 「H2. Heterographic puns will be more difficult for models to explain than homographic puns due to the former’s reliance on phonetic similarity, which is not explicitly encoded in orthographic text.」（出自：4.1 Hypotheses，第4页）
原文依据： 「The challenge likely stems from the additional complexity introduced by needing to recognise the phonetic similarity of the pun word whilst being trained on orthographic tokens.」（出自：5.1 Explanation Success Rate，第6页）
原文依据： 「Furthermore, we also observe that no existing state-of-the-art models can consistently explain jokes of any type correctly.」（出自：9 Conclusion，第8页）
知识点： 「汰渍洗衣球挑战」案例分析 题目： 在关于「汰渍洗衣球挑战」（Tide Pod Challenge）的案例研究中，大型模型（如GPT-4o）与小型模型在解释上的主要区别是什么？ 选项：
A. 小型模型提供了更长、更详细的解释。✅
B. 大型模型成功推断出笑话与「汰渍洗衣球挑战」这一网络现象的关联，而小型模型则没有。✅
C. 所有模型都未能理解这个笑话的背景。✅
D. 小型模型认为这个笑话不好笑，而大型模型认为它很幽默。✅
正确答案： B
原文依据： 「Interestingly, all of the full-size models inferred the reference to this challenge in their explanations. However, the smaller counterparts consistently omitted this information or presented misinterpretations…」（出自：7 Case Study，第7页）
原文依据： 「H4. Larger model variants will perform better than smaller variants, particularly for topical humour, due to being able to store larger amounts of information in their parameters…」（出自：4.1 Hypotheses，第4页）
原文依据： 「Regarding joke difficulty, the results align with the predicted ordering. Homographic jokes are the easiest to explain (β = 0.583, p < 0.001), while non-topical (β = -0.511, p < 0.001) and topical jokes (β = -0.574, p < 0.001) are significantly harder.」（出自：第7页，左栏底部）
知识点： LLM-Judge模型 题目： 为了验证人类评估者的可靠性，研究人员额外使用了一个「LLM作为评委」（LLM as a Judge）的范式。他们最终选用了哪个模型作为评委？ 选项：
A. GPT-4o✅
B. Llama 3.1-70B-Instruct✅
C. Qwen2.5-72B-Instruct✅
D. Gemini 1.5 Pro✅
正确答案： C
原文依据： 「Across the whole dataset, we additionally leverage the LLM as a Judge paradigm, using Qwen2.5-72B-Instruct, finding agreement for accuracy…」（出自：4.3 Evaluation Criteria，第5页）
原文依据： 「H3. Topical humour will be more difficult for models to explain than non-topical Reddit humour, due to the former’s reliance on subtle references to contemporary pop culture and events, rather than common sense reasoning and general knowledge.」（出自：4.1 Hypotheses，第4页）
原文依据： 「GPT-4o Mini, for example, explains the joke by suggesting that “teenagers are known for being messy eaters”, which caused increased sales.」（出自：第8页，左栏顶部）
原文依据： 「We present a novel, balanced dataset of 600 jokes, categorised into traditional puns…, non-topical Reddit jokes…, and topical Reddit jokes… Each joke is paired with a high-quality, succinct, human-authored reference explanation…」（出自：第2页，左栏）
原文依据： 「Homographic jokes, where a word has multiple meanings but identical spelling, consistently yield the highest proportion of successful explanations across nearly all models.」（出自：5.1 Explanation Success Rate，第6页）
Loakman, T. , Thorne, W., & Lin, C. (2025). ✅Comparing Apples to Oranges: A Dataset & Analysis of LLM Humour Understanding from Traditional Puns to Topical Jokes. arXiv:2507.13335v1 [cs.CL].
