#sarcasm-detection News & Analysis

4 articles tagged with #sarcasm-detection. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 235/10

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Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques

Researchers introduce Sarc7, a benchmark dataset for classifying seven types of sarcasm using large language models, with a novel emotion-based prompting technique that outperforms traditional zero-shot and few-shot approaches. The study demonstrates that Gemini 2.5 achieved the highest performance with an F1 score of 0.3664, while emotion-informed generation methods showed 38.46% improvement in human evaluation over baseline approaches.

🧠 Gemini

AINeutralarXiv – CS AI · Jun 56/10

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ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruity

Researchers introduce ProSarc, an audio-only machine learning framework that detects sarcasm by analyzing temporal mismatches between local prosodic patterns and overall emotional tone. The model achieves strong performance on multiple datasets (F1=75.3 on MUStARD++) and demonstrates cross-lingual generalization, advancing computational understanding of spoken sarcasm detection.

AINeutralarXiv – CS AI · Apr 106/10

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Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection

Researchers introduce Commander-GPT, a modular framework that orchestrates multiple specialized AI agents for multimodal sarcasm detection rather than relying on a single LLM. The system achieves 4.4-11.7% F1 score improvements over existing baselines on standard benchmarks, demonstrating that task decomposition and intelligent routing can overcome LLM limitations in understanding sarcasm.

🧠 GPT-4🧠 Gemini

AINeutralarXiv – CS AI · Mar 54/10

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MuSaG: A Multimodal German Sarcasm Dataset with Full-Modal Annotations

Researchers have released MuSaG, the first German multimodal sarcasm detection dataset featuring 33 minutes of annotated television content with text, audio, and video data. The study reveals a significant gap between human sarcasm detection (which relies heavily on audio cues) and current AI models (which perform best with text).