mlsanityData to 7 Oct 2026

Irony Detection, Reasoning and Understanding in Zero-shot Learning

arXiv 2501.16884 ↗First posted 28 Jan 2025cs.CL cs.AIcited by 5

On the map: LLM reasoning → Multimodal reasoning

Abstract (arXiv)

The generalisation of irony detection faces significant challenges, leading to substantial performance deviations when detection models are applied to diverse real-world scenarios. In this study, we find that irony-focused prompts, as generated from our IDADP framework for LLMs, can not only overcome dataset-specific limitations but also generate coherent, human-readable reasoning, transforming ironic text into its intended meaning. Based on our findings and in-depth analysis, we identify several promising directions for future research aimed at enhancing LLMs' zero-shot capabilities in irony detection, reasoning, and comprehension. These include advancing contextual awareness in irony detection, exploring hybrid symbolic-neural methods, and integrating multimodal data, among others.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 30 of its 69 references that are in the map. First posted on arXiv 28 Jan 2025.

Builds on

30 of 69 references.

References · 30

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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  7. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  8. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  9. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
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  11. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
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18 more
  1. 2023Large Language Models as Optimizerscited by 830
  2. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
  3. 2023A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPTcited by 612
  4. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 550
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  8. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 213
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  12. 2023ChatGPT is a Remarkable Tool -- For Expertscited by 16
  13. 2023Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysiscited by 14
  14. 2023Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processingcited by 11
  15. 2024ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Promptingcited by 8
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  17. 2022A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectivescited by 6
  18. 2024Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!cited by 5

Built on by

Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Are Large Language Models Sensitive to the Motives Behind Communication?cited by 7
  2. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challengescited by 3
  3. 2025SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detectioncited by 2
  4. 2025SeLeRoSa: Sentence-Level Romanian Satire Detection Datasetcited by 0
  5. 2025From Ground Trust to Truth: Disparities in Offensive Language Judgments on Contemporary Korean Political Discoursecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
  2. 2026Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts
  3. 2025SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection
  4. 2025Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection
  5. 2025CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models
  6. 2025Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models
  7. 2026"I See What You Did There": Can Large Vision-Language Models Understand Multimodal Puns?
  8. 2025Think Twice Before You Judge: Mixture of Dual Reasoning Experts for Multimodal Sarcasm Detection