Papers with text detoxification
DiffuDetox: A Mixed Diffusion Model for Text Detoxification (2023.findings-acl)
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Griffin Floto, Mohammad Mahdi Abdollah Pour, Parsa Farinneya, Zhenwei Tang, Ali Pesaranghader, Manasa Bharadwaj, Scott Sanner
| Challenge: | Existing text generation models that reduce toxicity of toxic text are inadequate for text detoxification tasks. |
| Approach: | They propose a conditional and unconditional diffusion model for text detoxification . conditional model takes toxic text as condition and reduces its toxicity . experimental results show the model achieves human-level fluency . |
| Outcome: | The proposed model reduces toxic text and produces diverse sentences . it can be used to train other models and ensure fluency . |
LLMs to Replace Crowdsourcing For Parallel Data Creation? The Case of Text Detoxification (2024.findings-emnlp)
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| Challenge: | Manual annotation methods, such as crowdsourcing, are costly and require intricate task design skills. |
| Approach: | They propose to use open source LLMs to annotate parallel data for text detoxification . they generate a pseudo-parallel detoxification dataset using activation patching . |
| Outcome: | The proposed model performs comparable to the original dataset in automatic detoxification evaluation metrics and superior quality in manual evaluation and side-by-side comparisons. |
XDetox: Text Detoxification with Token-Level Toxicity Explanations (2024.emnlp-main)
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| Challenge: | Existing methods for mitigating toxic content are black-box approaches, which results in limitations in modifying toxic tokens. |
| Approach: | They propose a method that integrates token-level toxicity explanations with the masking and infilling detoxification processes. |
| Outcome: | The proposed method outperforms baseline methods in fluency and toxicity reduction. |