DetoxLLM: A Framework for Detoxification with Explanations

• URL: http://arxiv.org/abs/2402.15951v2
• Date: Thu, 03 Oct 2024 23:53:45 GMT
• Title: DetoxLLM: A Framework for Detoxification with Explanations
• Authors: Md Tawkat Islam Khondaker, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan,
• Abstract summary: We propose DetoxLLM, the first comprehensive end-to-end detoxification framework. We first introduce a cross-platform pseudo-parallel corpus applying multi-step data processing and generation strategies. We show that our detoxification models outperform the SoTA model trained with human-annotated parallel corpus.
• Score: 25.174878638472254
• License:
• Abstract: Prior works on detoxification are scattered in the sense that they do not cover all aspects of detoxification needed in a real-world scenario. Notably, prior works restrict the task of developing detoxification models to only a seen subset of platforms, leaving the question of how the models would perform on unseen platforms unexplored. Additionally, these works do not address non-detoxifiability, a phenomenon whereby the toxic text cannot be detoxified without altering the meaning. We propose DetoxLLM, the first comprehensive end-to-end detoxification framework, which attempts to alleviate the aforementioned limitations. We first introduce a cross-platform pseudo-parallel corpus applying multi-step data processing and generation strategies leveraging ChatGPT. We then train a suite of detoxification models with our cross-platform corpus. We show that our detoxification models outperform the SoTA model trained with human-annotated parallel corpus. We further introduce explanation to promote transparency and trustworthiness. DetoxLLM additionally offers a unique paraphrase detector especially dedicated for the detoxification task to tackle the non-detoxifiable cases. Through experimental analysis, we demonstrate the effectiveness of our cross-platform corpus and the robustness of DetoxLLM against adversarial toxicity.

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