Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges | Paper Citation Record

Observation 5a7ca264-33bb-4193-85d6-7f0b37313266 · outbound

This paper cites Dataset Venue Data Forms Mechanism Size Avail.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Dataset Venue Data Forms Mechanism Size Avail

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.887946Z

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Observation ce667e96-4a8b-46ab-b7c0-0a9f328639f6 · outbound

This paper cites StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T15:37:55.837705Z

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Observation 9305302d-c688-48ec-9c6d-1bc8227980ed · outbound

This paper cites Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you!.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Can visual language models resolve textual ambiguity with visual cues? Let visual puns tell you!

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:37:55.740449Z

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Observation 9a85a9f7-a270-4326-9d3b-9bdf6aa76ff7 · outbound

This paper cites A Survey of Multimodal Sarcasm Detection.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges A Survey of Multimodal Sarcasm Detection

Reference 8

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Observation f0366a4a-bb40-426b-bbcc-eb726aabae70 · outbound

This paper cites Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.742624Z

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Observation 5799deb0-9e9a-4fb7-a4ad-16c6e369ac89 · outbound

This paper cites Decoding the underlying meaning of multimodal hateful memes.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Decoding the underlying meaning of multimodal hateful memes

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.099290Z

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Observation 2e9285cc-220b-4e79-8ff4-098fbec98f7c · outbound

This paper cites understanding.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges understanding

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.758264Z

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Observation 6cebd073-3be4-493b-93c7-0fea92ea8074 · outbound

This paper cites Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.762159Z

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Observation 0d600576-8618-40e0-9c9c-903d4b393815 · outbound

This paper cites MemeCap: A Dataset for Captioning and Interpreting Memes.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges MemeCap: A Dataset for Captioning and Interpreting Memes

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.765845Z

Source-reported events for the cited work

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Observation ff8b95d5-377f-4fda-adac-483ef7db909a · outbound

This paper cites Bottlehumor: Self-informed humor explanation using the information bottleneck principle.arXiv preprint arXiv:2502.18331,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Bottlehumor: Self-informed humor explanation using the information bottleneck principle.arXiv preprint arXiv:2502.18331,

Reference 18

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:37:55.574426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9e0af90-c0e8-459c-90b5-bbacb26e995a · outbound

This paper cites MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.773203Z

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Observation 76ea2557-46e7-4d9e-9ded-308bcb254940 · outbound

This paper cites D-humor: Dark humor understanding via multimodal open-ended reasoning–a benchmark dataset and method.arXiv preprint arXiv:2509.06771,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges D-humor: Dark humor understanding via multimodal open-ended reasoning–a benchmark dataset and method.arXiv preprint arXiv:2509.06771,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.776995Z

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Observation 9dc9cc02-3763-4fd8-8c9f-bbec4b384e0a · outbound

This paper cites Hope ‘the paragraph guy’explains the rest: Introducing mesum, the meme summarizer.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Hope ‘the paragraph guy’explains the rest: Introducing mesum, the meme summarizer

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.088382Z

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Observation f037840f-a3a0-4afc-9a93-a8d360e0fb30 · outbound

This paper cites Looking beyond the pixels: Evaluating visual metaphor understanding in vlms.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Looking beyond the pixels: Evaluating visual metaphor understanding in vlms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.068050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b2fbfc4a-84c2-49e2-9fa9-a33ecc347486 · outbound

This paper cites Are vision-language models safe in the wild? a meme-based benchmark study.arXiv preprint arXiv:2505.15389,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Are vision-language models safe in the wild? a meme-based benchmark study.arXiv preprint arXiv:2505.15389,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.791795Z

Source-reported events for the cited work

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Observation 09f79d5a-abcd-4ac5-aa13-170674144430 · outbound

This paper cites Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.799232Z

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Observation 8bd2702c-1527-4593-b54d-887b06ed3da6 · outbound

This paper cites Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:37:55.371632Z

Source-reported events for the cited work

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Observation 8164d6d8-2282-401e-a94c-9d019b630a93 · outbound

This paper cites G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.807289Z

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Observation 6455cf32-b6f7-4270-a15c-1bc7e6ea22c0 · outbound

This paper cites Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Inference-time scaling for generalist reward modeling.arXiv preprint arXiv:2504.02495,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.811442Z

Source-reported events for the cited work

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Observation dbdae401-31eb-4ac4-b830-0a3e726c7238 · outbound

This paper cites Comicorda: Dialogue act recognition in comic books.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Comicorda: Dialogue act recognition in comic books

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.057228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 77107616-dd70-4a5b-bf70-44dc5e811593 · outbound

This paper cites YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.820038Z

Source-reported events for the cited work

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Observation 3d3eebc3-281f-4c1e-9d41-a48f9b77f061 · outbound

This paper cites Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Which LLMs Get the Joke? Probing Non-STEM Reasoning Abilities with HumorBench

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.823963Z

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Observation baff2657-b0d6-4d55-a9b4-fa74f8d763fc · outbound

This paper cites Benchmarking vision language models for cultural understanding.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Benchmarking vision language models for cultural understanding

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.046639Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 36fcb0df-0172-4110-9959-6392ed7f9120 · outbound

This paper cites Laugh, relate, engage: Stylized comment generation for short videos.arXiv preprint arXiv:2511.03757,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Laugh, relate, engage: Stylized comment generation for short videos.arXiv preprint arXiv:2511.03757,

Reference 34

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:37:55.263190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4f5240d-d1e1-4f88-9d9f-d49133c50719 · outbound

This paper cites Yamshchikov.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Yamshchikov

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.035374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e01a92f4-97ba-40ef-beb9-16bdb2e2bcb8 · outbound

This paper cites ISBN 979-8-89176-332-6.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges ISBN 979-8-89176-332-6

Reference 36

Resolution: verified exact; doi, observed 2026-08-15T15:37:55.015753Z

Source-reported events for the cited work

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Observation 7a6d68b7-8fcd-4d4c-8e81-ccb027d6ac09 · outbound

This paper cites Can Large Language Models Understand Symbolic Graphics Programs?.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Can Large Language Models Understand Symbolic Graphics Programs?

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.843886Z

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Observation 5f27c790-cbcd-4b2d-8746-ae53afd53fce · outbound

This paper cites Understanding figurative meaning through explainable visual entailment.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Understanding figurative meaning through explainable visual entailment

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.014548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1dd2b970-a63d-4537-a686-6de010c28d04 · outbound

This paper cites MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges MemeCLIP: Leveraging CLIP Representations for Multimodal Meme Classification

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.855100Z

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Observation d4ce5a6b-a3a0-4e05-9631-dae33e44f472 · outbound

This paper cites SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.863025Z

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Unavailable: canonical work link unavailable.

Observation 7f541c88-e444-49fa-80d5-8a37ecc53d64 · outbound

This paper cites DISARM: Detecting the Victims Targeted by Harmful Memes.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges DISARM: Detecting the Victims Targeted by Harmful Memes

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.866988Z

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Observation 2cd11f27-bed5-4a98-94c8-e85c0ab99dbb · outbound

This paper cites doi: 10.18653/v1/2024.findings-naacl.152.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges doi: 10.18653/v1/2024.findings-naacl.152

Reference 45

Resolution: verified exact; doi, observed 2026-08-15T15:37:55.004077Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e04ed06-7716-4fe0-a4a7-7cfe4788b48f · outbound

This paper cites Humor Mechanics: Advancing Humor Generation with Multistep Reasoning.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Humor Mechanics: Advancing Humor Generation with Multistep Reasoning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.879354Z

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Observation a27fcbbe-a56b-4b19-a426-3dc6e3c356fe · outbound

This paper cites Memecraft: Contextual and stance-driven multimodal meme generation.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Memecraft: Contextual and stance-driven multimodal meme generation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.993042Z

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Observation 4d7b9089-1cee-424c-a65b-f1fd644b1d11 · outbound

This paper cites Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.886616Z

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Observation 36f811d6-8500-4dad-bb74-df8e5607c4d1 · outbound

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Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:37:55.980917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30ae59e4-0394-4a96-8f9b-de53084884b3 · outbound

This paper cites ISBN 979-8-89176-335-7.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges ISBN 979-8-89176-335-7

Reference 50

Resolution: verified exact; doi, observed 2026-08-15T15:37:54.991313Z

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Observation 6abb19dc-67c1-4d28-bbd5-d7a7fe1ab650 · outbound

This paper cites Taxonomy of risks posed by language models.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Taxonomy of risks posed by language models

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.969066Z

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Observation 1ccd4db3-e620-4d4f-a8f8-ad98616a713a · outbound

This paper cites VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges VisuLogic: A Benchmark for Evaluating Visual Reasoning in Multi-modal Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.901478Z

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Observation 5db5aa17-39c1-44b1-b14f-595ccf37e9e2 · outbound

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Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-15T15:37:55.957927Z

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Observation bdb394f1-c56e-4d54-9141-81eedb0aafa7 · outbound

This paper cites doi: 10.18653/v1/2024.findings-acl.113.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges doi: 10.18653/v1/2024.findings-acl.113

Reference 54

Resolution: verified exact; doi, observed 2026-08-15T15:37:54.979323Z

Source-reported events for the cited work

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Observation 4fff20a7-a3ce-4494-98e7-bf3c8bd61ffe · outbound

This paper cites Mmoe: Enhancing multimodal models with mixtures of multimodal interaction experts.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Mmoe: Enhancing multimodal models with mixtures of multimodal interaction experts

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.946816Z

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Observation f71c3679-0e50-4362-b547-797fbae8565e · outbound

This paper cites Image matters: A new dataset and empirical study for multimodal hyperbole detection.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Image matters: A new dataset and empirical study for multimodal hyperbole detection

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.935474Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3c695397-1a67-4e02-bf17-65d96c488fe7 · outbound

This paper cites Humorchain: Theory-guided multi-stage reasoning for interpretable multimodal humor generation.arXiv preprint arXiv:2511.21732,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Humorchain: Theory-guided multi-stage reasoning for interpretable multimodal humor generation.arXiv preprint arXiv:2511.21732,

Reference 57

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Observation 7a038250-a7cc-41aa-98cd-b9ae50f9b3fa · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges BERTScore: Evaluating Text Generation with BERT

Reference 58

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Observation 406b73d3-bc36-409a-8c86-2bfb139ffb34 · outbound

This paper cites MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.927202Z

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Observation 817a64e1-4b4c-45c0-92d1-055c68298caf · outbound

This paper cites Social meme-ing: Measuring linguistic variation in memes.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Social meme-ing: Measuring linguistic variation in memes

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.923469Z

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Observation c7c03f8a-7dee-4e75-8886-38ea8f157a11 · outbound

This paper cites For each benchmark, we retain the task definition, prompt format, answer format, evaluation split, and scoring procedure reported in the corresponding original paper.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges For each benchmark, we retain the task definition, prompt format, answer format, evaluation split, and scoring procedure reported in the corresponding original paper

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.910105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6584ebf8-d2aa-4c28-b0bc-d4db8bbdaa93 · outbound

This paper cites For open-source models, decoding is performed withdo_sam- ple=true; all remaining benchmark-specific generation and evaluation settings follow the corresponding original papers.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges For open-source models, decoding is performed withdo_sam- ple=true; all remaining benchmark-specific generation and evaluation settings follow the corresponding original papers

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.899085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28833bbf-2686-4f3b-8416-e918fc109f41 · outbound

This paper cites Dataset Venue Mechanism Size Avail.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Dataset Venue Mechanism Size Avail

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:55.876660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9a79e38-a163-4ac9-95e0-f72c01ae8cee · outbound

This paper cites Mememind: A large-scale multimodal dataset with chain-of-thought reasoning for harmful meme detection.arXiv preprint arXiv:2506.18919,.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Mememind: A large-scale multimodal dataset with chain-of-thought reasoning for harmful meme detection.arXiv preprint arXiv:2506.18919,

Reference 1980

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.738552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aaea0a97-e0fa-43a2-be9c-2e76bfb16366 · outbound

This paper cites Memedetoxnet: Balancing toxicity reduction and context preservation.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Memedetoxnet: Balancing toxicity reduction and context preservation

Reference 1996

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.078393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dc61cc92-c700-4f1b-8a27-a7deeed866cd · outbound

This paper cites Spoken in jest, detected in earnest: A systematic review of sarcasm recognition-multimodal fusion, challenges, and future prospects.IEEE Transactions on Affective Computing, 2025a.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Spoken in jest, detected in earnest: A systematic review of sarcasm recognition-multimodal fusion, challenges, and future prospects.IEEE Transactions on Affective Computing, 2025a

Reference 2004

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.109645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6c068e94-25e3-4955-830d-ecc2a85a9522 · outbound

This paper cites Content-specific humorous image captioning using incongruity resolution chain-of-thought.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Content-specific humorous image captioning using incongruity resolution chain-of-thought

Reference 2013

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.003868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5dd47e9d-764c-4cf3-8b52-4873c728473d · outbound

This paper cites ViPE: Visualise Pretty-much Everything.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges ViPE: Visualise Pretty-much Everything

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.858970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8929c3-7cd6-4d31-8aaa-9bb3962cda7d · outbound

This paper cites Are we on the right way for evaluating large vision-language models?Advances in Neural Information Processing Systems, 37:27056–27087, 2024a.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Are we on the right way for evaluating large vision-language models?Advances in Neural Information Processing Systems, 37:27056–27087, 2024a

Reference 2016

Resolution: verified exact; raw_fallback, observed 2026-08-15T15:37:55.809005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b83ecbf-e857-4273-9f6c-694124d0eac0 · outbound

This paper cites I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges I Spy a Metaphor: Large Language Models and Diffusion Models Co-Create Visual Metaphors

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.712678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9a39e3f-da65-4e95-b6ec-0870bd17495d · outbound

This paper cites MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:37:55.865316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4823a639-7400-46b0-9bbc-5543cc892cb5 · outbound

This paper cites TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges TextMI: Textualize Multimodal Information for Integrating Non-verbal Cues in Pre-trained Language Models

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:37:55.631750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b6c4a030-f7f2-4af0-ad0c-7875208979f8 · outbound

This paper cites When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.795252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6da4756f-d207-4f5c-bda4-6ae6ad2820c9 · outbound

This paper cites Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba

Reference 2023

Resolution: verified exact; local_arxiv, observed 2026-08-15T15:37:55.614956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cefcce8f-bc2d-4b9a-8e28-49a7ddbdd6b4 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.726135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 206695ef-28b5-485b-a2e3-249cdf68cf34 · outbound

This paper cites Qwen3-VL Technical Report.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Qwen3-VL Technical Report

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T15:37:54.703587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8765c1e6-1bb2-44ee-8e84-5b046f87cc9d · outbound

This paper cites Humor in pixels: Benchmarking large multimodal models understanding of online comics.

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges Humor in pixels: Benchmarking large multimodal models understanding of online comics

Reference 2026

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:37:56.024748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers