Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts
arXiv:2606.16845v1 Announce Type: new Abstract: Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot irony detection a persistent challenge. We introduce the Robust Dual-Signal (RDS) Fusion framework, a hybrid neuro-symbolic architecture that compresses Chain-of-Thought (CoT) reasoning trajectories without Supervised Fine-Tuning (SFT). Evaluated on a strictly held-out TweetEval test set (N=734), RDS achieves 78.1% accuracy and a Macro F1 of 0.777, matching the absolute performance ceiling of the fine-tuned BERTweet. On the heavily imbalanced iSarc
The persistent challenge of irony detection for LLMs, combined with the rapid advancements in neuro-symbolic AI, creates the opportune moment for hybrid solutions that leverage the strengths of both paradigms.
This development indicates a significant leap in LLM's capacity for nuanced understanding of human language, pushing beyond literal interpretation towards more sophisticated contextual and social comprehension.
The ability of LLMs to detect complex linguistic phenomena like irony, without extensive fine-tuning, dramatically expands their utility in social media analysis, content moderation, and human-computer interaction.
- · AI developers
- · Social media platforms
- · Content moderation services
- · Marketing analytics
- · Basic sentiment analysis tools
- · Zero-shot LLM irony detection (previous methods)
More accurate and efficient automated understanding of complex human sentiment in digital text.
Improved ability for AI assistants and chatbots to engage in more 'human-like' and contextually aware conversations.
Potential for new forms of media analysis and social trend detection that capture subtle human intentions and humor at scale.
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Read at arXiv cs.CL