用混合神经符号方法让小模型精准识别社交媒体讽刺语，无需大量标注数据。
Robust Dual-Signal Fusion: Hybrid Neuro-Symbolic Gating with Compressed Chain-of-Thought Refinement for Irony Detection in Social Media Texts
- 融合神经与符号逻辑，压缩思维链推理路径，不依赖微调。
- 在推特数据集上准确率78.1%，讽刺检测宏平均F1达0.777。
- 适合资源有限但需高鲁棒性的讽刺识别场景，尤其应对噪声文本。
小规模大语言模型天然倾向于字面语义理解，使得在嘈杂的用户生成文本中进行少样本讽刺检测始终是难题。我们提出鲁棒双信号（RDS）融合框架，一种无需监督微调即可压缩思维链（CoT）推理轨迹的混合神经符号架构。在严格预留的TweetEval测试集（N=734）上，RDS达到78.1%准确率和0.777的宏平均F1，与微调后的BERTweet性能持平。在严重不平衡的iSarcasm数据集上，冻结的CoT流水线过滤掉22.5%的分布外幻觉，实现少样本宏平均F1 0.6726与讽刺类F1 0.4821，优于多个高度监督的SemEval Transformer集成模型。统计消融实验表明：仅添加符号先验对神经基线提升不显著；而RDS融合与罗伯塔+符号先验组合相比亦无显著差异；但其并行融合显著优于独立基线（p=0.005）。
原文摘要 · Abstract (English)
Small-scale Large Language Models (LLMs) natively default to literal semantic interpretations, making few-shot irony detection a persistent challenge in noisy, user-generated text. 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 a fine-tuned BERTweet. On the heavily imbalanced iSarcasm dataset, the frozen CoT pipeline filters $22.5\%$ of out-of-distribution hallucinations, yielding a few-shot Macro F1 of $0.6726$ and Ironic F1 of $0.4821$, outperforming multiple heavily supervised SemEval transformer ensembles. Statistical ablation confirms this structural synergy: while adding the symbolic prior to the neural baseline yields an insignificant gain, and the RDS fusion is statistically insignificant compared to the combined RoBERTa and symbolic prior ablation; the concurrent fusion achieves a statistically significant improvement over the standalone baseline ($p=0.005$).
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