Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts

2026-06-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

The Robust Dual-Signal (RDS) Fusion framework introduces a hybrid neuro-symbolic architecture designed to enhance irony detection in social media texts, specifically addressing Large Language Models' (LLMs) native literal semantic interpretations. This framework compresses Chain-of-Thought (CoT) reasoning trajectories without requiring Supervised Fine-Tuning (SFT). Evaluated on a strictly held-out TweetEval test set (N=734), RDS achieved 78.1% accuracy and a Macro F1 of 0.777, matching the performance ceiling of the fine-tuned BERTweet. On the heavily imbalanced iSarcasm dataset, its frozen CoT pipeline filtered 22.5% of out-of-distribution hallucinations, yielding a zero-shot Macro F1 of 0.6726 and Ironic F1 of 0.4821, outperforming multiple heavily supervised SemEval transformer ensembles. A statistical ablation confirmed that only the complete, concurrent fusion of all three signals—neural baseline, symbolic prior, and CoT pipeline—achieves a statistically validated improvement (p = 0.005).

Key takeaway

For NLP Engineers developing robust irony detection systems, especially those leveraging Large Language Models, consider implementing a hybrid neuro-symbolic fusion approach. Your systems can achieve performance comparable to fine-tuned models in zero-shot settings by concurrently integrating neural baselines, symbolic priors, and compressed Chain-of-Thought reasoning. This method offers a path to filter out-of-distribution hallucinations and significantly improve accuracy without extensive supervised fine-tuning, streamlining deployment for nuanced social media text analysis.

Key insights

Hybrid neuro-symbolic fusion with compressed Chain-of-Thought effectively overcomes LLM literalism for zero-shot irony detection.

Principles

• Complete fusion of neural, symbolic, and CoT signals is critical for significant gains.
• Individual signal additions may not yield statistically significant improvements.

Method

The RDS Fusion framework employs hybrid neuro-symbolic gating with compressed Chain-of-Thought refinement to process dual signals for irony detection.

In practice

• Explore compressing CoT reasoning without Supervised Fine-Tuning for specific tasks.
• Integrate symbolic priors with neural models and CoT for robust performance.

Topics

• Irony Detection
• Neuro-Symbolic AI
• Large Language Models
• Chain-of-Thought
• Social Media Text
• Model Fusion

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.