ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection

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Abstract

Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped, primarily due to the lack of culturally relevant, large-scale datasets. In this work, we introduce TOXIFRENCH, a new public benchmark of 53,622 French online comments, constructed via a semi-automated annotation pipeline that reduces manual labeling to only 10% through high-confidence LLM-based pre-annotation and human verification. Then, we benchmark a broad range of models and uncover a counterintuitive insight: Small Language Models (SLMs) outperform many larger models in robustness and generalization under the toxicity detection task. Motivated by this finding, we propose a novel Chain-of-Thought (CoT) fine-tuning strategy using a dynamic weighted loss that progressively emphasizes the model's final decision, significantly improving faithfulness. Our fine-tuned 4B model achieves state-of-the-art performance, improving its F1 score by 13% over its baseline and outperforming LLMs such as GPT-40 and Gemini-2.5. Further evaluation on a cross-lingual toxicity benchmark demonstrates strong multilingual ability, suggesting that our methodology can be effectively extended to other languages and safety-critical classification tasks.

Key Contributions

• Dataset and benchmark: Introduction of ToxiFrench, a new public benchmark dataset for French toxicity detection (53,622 entries).
• Evaluation state-of-the-art detectors: Extensive evaluation of LLMs (GPT-4o, DeepSeek, Gemini, Mistral, ...), SLMs (Qwen, Gemma, Mistral, ...), Transformers (CamemBERT, DistilBERT, ...), and moderation APIs (Perspective API, OpenAI moderation, Mistral moderation, ...), showing that SLMs outperform LLMs in robustness to bias, cross-language consistency, and generalization to novel toxicity forms.
• Transfer learning strategies: Systematic comparison of ICL, SFT, and CoT reasoning.
• Model development: Development of a state-of-the-art 4B SLM for French toxicity detection that outperforms several powerful LLMs based on the Qwen3-4B model.
• CoT fine-tuning: Introduction of a novel approach for CoT fine-tuning that employs a dynamic weighted loss function, significantly boosting performance by ensuring the model's reasoning is faithful to its final conclusion.

Citing This Work

                If you use this dataset, model, or methodology in your research, please cite our work using the following BibTeX entry:            

@misc{delaval2025toxifrench,
    title={ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection},
    author={Axel Delaval and Shujian Yang and Haicheng Wang and Han Qiu and Jialiang Lu},
    year={2025},
}