Papers with detoxification methods
Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models. (2022.acl-srw)
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| Challenge: | Existing methods of textual style transfer are monolingual i.e. designed to work in one exact language. |
| Approach: | They propose to make large multilingual models capable of performing multilingual style transfer without direct fine-tuning in a given language. |
| Outcome: | The proposed model can generate text in a given language without fine-tuning and is able to perform cross-lingual detoxification without direct fine- tuning. |
CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)
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| Challenge: | Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality. |
| Approach: | They propose a text detoxification framework that pays attention to both context and detoxification process. |
| Outcome: | Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines. |
On Localizing and Deleting Toxic Memories in Large Language Models (2025.findings-naacl)
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Anubrata Das, Manoj Kumar, Ninareh Mehrabi, Anil Ramakrishna, Anna Rumshisky, Kai-Wei Chang, Aram Galstyan, Morteza Ziyadi, Rahul Gupta
| Challenge: | Existing methods to reduce toxic generation in large language models are not fully understood. |
| Approach: | They propose to understand the mechanisms that drive toxic generation in large language models by using memory localization to reduce toxic generation. |
| Outcome: | The proposed method reduces toxic generation from 62.86% to 28.61%, but it also improves generation quality. |
Detoxifying Language Models Risks Marginalizing Minority Voices (2021.naacl-main)
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| Challenge: | Existing detoxification techniques have been proposed to mitigate toxic LM generations . e.g., detoxification makes LMs more brittle to distribution shift, especially on language used by marginalized groups . |
| Approach: | They propose to use detoxification techniques to reduce toxic LM generations without affecting perplexity or generation quality on nontoxic inputs. |
| Outcome: | The proposed methods hurt equity on language used by marginalized groups, the authors show . they show that detoxification makes LMs more brittle to distribution shift, they say . |
DSCD: Large Language Model Detoxification with Self-Constrained Decoding (2025.emnlp-main)
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| Challenge: | Existing methods for decoding large language models (LLMs) are based on external constraints and require additional resource overhead and loss of generation fluency. |
| Approach: | They propose a method for LLMs detoxification without parameter fine-tuning that strengthens the inner token distribution while weakening that of hallucination and toxic layer during output generation. |
| Outcome: | Extensive experiments on open-source LLMs and public datasets demonstrate DSCD's state-of-the-art (SOTA) performance in detoxification and generation fluency, with superior efficiency compared to existing methods. |
Breaking Bad Tokens: Detoxification of LLMs Using Sparse Autoencoders (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are ubiquitous in user-facing applications, yet they still generate undesirable toxic outputs, including profanity, vulgarity, and derogatory remarks. |
| Approach: | They leverage sparse autoencoders to identify toxicity-related directions in residual stream of large language models and perform targeted activation steering using the corresponding decoder vectors. |
| Outcome: | The proposed models surpass baselines in reducing toxicity by up to 20%, though fluency can degrade noticeably on GPT-2 Small and Gemma-2-2B. |
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. |
Detoxification for LLM: From Dataset Itself (2026.acl-long)
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| Challenge: | Existing methods for large language models focus on post-training stage or inference time, while few tackle the source of toxicity, namely, the dataset itself. |
| Approach: | They propose to localize and rewrite toxic spans in raw corpora with SoCD, which guides an LLM to localized and preserving semantics while preserving toxicity. |
| Outcome: | The proposed method reduces TP from 0.42 to 0.18 and Expected Maximum Toxicity (EMT) from 0.43 to 0.20 on three LLMs. |
Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization (2025.emnlp-main)
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| Challenge: | Existing methods for detoxification of text often rely on manually annotated data . xiangli: "detoxification of texts is a powerful way to remove toxic content" |
| Approach: | They propose a reinforcement learning framework that optimizes detoxification and semantic preservation without annotating large amounts of data. |
| Outcome: | The proposed method overcomes major limitations and surpasses humanannotated references across multiple benchmarks. |