Irony detection in natural language processing is hard. It is hard because of its subtle contextual cues, and existing models often struggle to generalize across diverse datasets.
This paper proposes IDADP, a framework leveraging ChatGPT's zero-shot capabilities. It combines domain-specific knowledge generation with prompt engineering techniques to enhance irony detection, reasoning, and understanding.
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https://arxiv.org/abs/2501.16884
📌 IDADP smartly uses the LLM's existing knowledge. It extracts and integrates domain knowledge through specifically crafted question patterns. This approach reduces reliance on extensive training data. It improves zero-shot performance.
📌 The framework strategically combines various prompting techniques. It uses zero-shot, domain-specific Chain-of-Thought, and meta-prompting. This method directly activates relevant parts of a pre-trained model. This approach targets nuanced understanding.
📌 IDADP uses a voting mechanism over multiple prompt outputs. It increases robustness against prompt variability. It leverages probabilistic outputs for each classification. This provides a confidence measure for irony detection.
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Methods Explored in this Paper 🔧:
→ The IDADP framework first generates domain-specific knowledge. It uses question patterns like Flipped Interaction, Persona, Question Refinement, and Recipe.
→ It then integrates this knowledge into contextual cues. It creates definition of Irony, uses domain specificity features and the full process for irony detection.
→ It applies prompt engineering, using zero-shot prompting, domain-specific Chain-of-Thought, meta prompting, and probabilistic classification. It aggregates results through a majority-voting mechanism.
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Key Insights 💡:
→ LLMs like ChatGPT show potential for irony detection, and it is very highly dependent on prompt design.
→ Contextual and linguistic nuances are important for irony. Dataset biases impact model generalization.
→ Reasoning in LLMs can be improved. Integrating explicit knowledge improves structured and consistent reasoning.
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Results 📊:
→ IDADP outperforms baselines, like Zero-shot Chain of Thought, Auto-Chain of Thought, Automatic Prompt Engineer, Plan and Solve Prompting across six datasets, with a 0.67 F1 score on iSarcasm, compared to a maximum of 0.60 for other methods.
→ IDADP had better human evaluation scores, with the highest mean rating of 2.5 in the reasoning task, showing that its reasoning is much better compared to other methods.
→ IDADP achieves cosine similarity scores between 0.7 and 0.9 for aligning generated meaning with intended ironic meaning.
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