When Does Span-Guided Detoxification Help? Human Preferences and Evaluator Diagnostics in a Controlled Comparison
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Abstract (arXiv)
Span-guided rewriting aims to preserve meaning by localizing edits to annotated harmful spans, but the same constraint can leave harmful intent insufficiently mitigated. We present a controlled exploratory comparison of span-guided and unguided detoxification on a mixed-source English evaluation set comprising manually curated inputs and HateXplain test items. We conduct a dense blinded human evaluation under a fixed single-generator setting. Human preferences reveal a trade-off rather than a uniformly superior rewriting strategy. Span-guided outputs are favored when localized editing preserves the original stance and avoids unnecessary modification, whereas unguided outputs are favored when broader rewriting achieves more complete mitigation. This contrast varies substantially across the study-defined strata: the two strategies are competitive in the strong stratum, while unguided rewriting is clearly preferred in the mild stratum. Rationale annotations trace this difference to complementary failure risks: residual harm after localized editing and over-modification after broader rewriting. We treat automatic evaluation as a diagnostic rather than a substitute for human judgment. Toxicity-similarity scalarizations, a multi-generator analysis, and two general-purpose LLM judges reproduce parts of the aggregate tendency but do not yield an analogous stratified contrast. These setting-specific findings do not establish a severity-based routing rule. Instead, they motivate evaluation protocols that assess mitigation sufficiency and meaning preservation separately and report both residual harm and over-modification alongside aggregate scores.
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References · 10
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 729
- 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 420
- 2020GeDi: Generative Discriminator Guided Sequence Generationcited by 377
- 2022A New Generation of Perspective API: Efficient Multilingual Character-level Transformerscited by 181
- 2020Deep Learning for Text Style Transfer: A Surveycited by 161
- 2021A Review of Text Style Transfer using Deep Learningcited by 18
- 2024DetoxLLM: A Framework for Detoxification with Explanationscited by 10
- 2023HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Modelscited by 8
- 2023Correction of Errors in Preference Ratings from Automated Metrics for Text Generationcited by 3
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