SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models

• URL: http://arxiv.org/abs/2509.15174v2
• Date: Wed, 08 Oct 2025 03:00:50 GMT
• Title: SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models
• Authors: Huy Nghiem, Advik Sachdeva, Hal Daumé III,
• Abstract summary: We introduce SMARTER, a framework for explainable content moderation using Large Language Models (LLMs)<n>In Stage 1, we leverage LLMs' own outputs to generate synthetic explanations for both correct and incorrect labels.<n>In Stage 2, we refine explanation quality through cross-model training, allowing weaker models to align stylistically and semantically with stronger ones.
• Score: 23.6287295499467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 13.5% 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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