Model overview
Garak Refusal Detector is a binary sequence classifier model that detects refusal responses in LLM outputs. The model is built as a semantic replacement for string-based keyword detectors (e.g., Garak's MitigationBypass detector), enabling refusal detection based on meaning rather than surface patterns. Developers integrating the Garak framework can use this model to detect refusal responses in LLM outputs. It serves as an alternative to keyword‑based detectors, using a transformer‑based classifier that returns a binary signal indicating refusal or non‑refusal. Garak Refusal Detector is a fine-tuned version of answerdotai/ModernBERT-base, trained on synthetic refusal and non-refusal data for binary sequence classification.
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Public benchmarks & run configuration
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Sources & traceability
许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。