用自生成解释提升毒性内容检测，少数据也能高精度。
SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
- 让大模型自动生成真假标签的解释，减少人工标注依赖。
- 在三个数据集上比基线模型宏平均F1提升13%。
- 适合资源有限但需可解释性的内容审核场景。
为应对社交媒体上毒性内容的泛滥，本文提出SMARTER，一种基于大语言模型（LLM）的高效两阶段可解释内容审核框架。第一阶段利用LLM自身输出生成正确与错误标签的合成解释，通过偏好优化实现对齐，仅需极少人工监督。第二阶段通过跨模型训练优化解释质量，使弱模型在风格和语义上向强模型对齐。在HateXplain、Latent Hate和Implicit Hate三个基准任务上的实验表明，SMARTER在仅使用少量训练数据的情况下，使LLM的宏平均F1最高提升13%，显著优于标准少样本基线。该框架通过激发大模型自我优化能力，为低资源场景下的可解释性内容检测提供了可扩展策略。
原文摘要 · Abstract (English)
WARNING: This paper contains examples of offensive materials. To address the proliferation of toxic content on social media, we introduce SMARTER, we introduce SMARTER, a data-efficient two-stage framework for explainable content moderation using Large Language Models (LLMs). In Stage 1, we leverage LLMs' own outputs to generate synthetic explanations for both correct and incorrect labels, enabling alignment via preference optimization with minimal human supervision. In Stage 2, we refine explanation quality through cross-model training, allowing weaker models to align stylistically and semantically with stronger ones. Experiments on three benchmark tasks -- HateXplain, Latent Hate, and Implicit Hate -- demonstrate that SMARTER enables LLMs to achieve up to a 13% macro-F1 improvement over standard few-shot baselines while using only a fraction of the full training data. Our framework offers a scalable strategy for low-resource settings by harnessing LLMs' self-improving capabilities for both classification and explanation.
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