@inproceedings{delaval-etal-2026-toxifrench,
    title = "{TOXIFRENCH}: Benchmarking and Enhancing Language Models via {C}o{T} Fine-Tuning for {F}rench Toxicity Detection",
    author = "Delaval, Axel  and
      Yang, Shujian  and
      Wang, Haicheng  and
      Qiu, Han  and
      LU, Jialiang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1074/",
    pages = "21354--21375",
    ISBN = "979-8-89176-395-1",
    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, human-annotated, large-scale datasets. In this work, we release TOXIFRENCH, a dataset of 53,622 French online comments, together with a 1,388-sample balanced benchmark split for systematic evaluation. The dataset is constructed via a semi-automated annotation pipeline that reduces manual labeling to only 10{\%} through high-confidence LLM-based pre-annotation and human verification, while ensuring statistically near-perfect alignment with human-only annotation. We then benchmark a broad range of models and uncover a counterintuitive insight: Small Language Models (SLMs) often surpass larger models in robustness and generalization on this 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 (Qwen3-4B) achieves state-of-the-art performance on the benchmark, improving its balanced accuracy by 10{\%} over its baseline and achieving better performance than GPT-4o and DeepSeek-R1 on our benchmark, while successfully retaining cross-lingual capabilities."
}