DetoxLLM: A Framework for Detoxification with Explanations
<|reference_start|>DetoxLLM: A Framework for Detoxification with Explanations: 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.<|reference_end|>
@article{khondaker2024detoxllm:,
    title={DetoxLLM: A Framework for Detoxification with Explanations},
    author={Md Tawkat Islam Khondaker, Muhammad Abdul-Mageed, Laks V. S.
  Lakshmanan},
    journal={arXiv preprint arXiv:2402.15951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15951},
    primaryClass={cs.LG cs.CL cs.CY}
}