arXiv:2602.02498v2 Announce Type: replace-cross Abstract: Large language models can produce toxic or inappropriate text even for benign inputs, creating risks when deployed at scale. Detoxification is therefore important for safety and user trust, particularly when we want to reduce harmful content without sacrificing the model's generation quality. Many existing approaches rely on model retraining, gradients, or learned auxiliary components, which can be costly and may not transfer across model families or to truly black-box settings. We introduce a test-time procedure that approximates the g
The proliferation of powerful large language models necessitates immediate solutions for mitigating harmful outputs without extensive retraining, pushing research towards efficient, black-box detoxification methods.
Ensuring the safety and ethical deployment of large language models is critical for public trust, regulatory acceptance, and widespread adoption across sensitive applications.
This innovation introduces a method for detoxifying LLMs at test-time, reducing reliance on costly retraining or modification of the base model, enabling broader application and faster iteration on safety features.
- · LLM developers
- · AI safety researchers
- · AI-powered product companies
- · Companies relying solely on post-hoc human moderation for harmful content
- · Methods requiring model retraining for every safety update
Reduced deployment risks for large language models will accelerate their integration into sensitive applications.
Easier detoxification could lead to a proliferation of more powerful, yet also potentially more harmful, base models.
The development of adversarial test-time detoxification techniques might emerge, leading to an arms race in AI safety.
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Read at arXiv cs.LG