HF_Detox at TextDetox CLEF 2025: Prompt-Driven Multilingual Detoxification

The text detoxification task aims to automatically transform toxic or offensive sentences into neutral, semantically equivalent paraphrases. In this study, we evaluated a lightweight, prompt-based chain-of-thought approach using OpenAI’s GPT-4o-mini on the PAN 2025 multilingual ParaDetox benchmark, encompassing 15 typologically diverse languages. Without any fine-tuning, our method leverages a single system instruction with a few-shot setup. During the initial evaluation, our approach achieved top-3 Joint (J) scores in six languages, most notably French (J = 0.775, rank 2) and Hebrew (J = 0.613, rank 1), and ranked in the top 10 for 9 languages overall. In the post-evaluation phase, our system ranked fourth overall in the parallel and third non-parallel data tracks, achieving average J scores of 0.768 and 0.718, respectively. Strong results were observed in English (J = 0.775), Spanish (J = 0.814), Japanese (J = 0.819), and Hebrew (J = 0.671), achieving the second position, demonstrating our method’s robustness in both high-resource and morphologically diverse languages. These findings underscore the potential of multilingual large language models, guided by carefully designed prompts, to serve as plug-and-play detoxifiers even in the absence of task-specific fine-tuning.