Unverified50% confidenceBenchmarkExact time
Experiments show that models processed with abliteration exhibit almost no degradation on standard capability evaluations, but the probability of refusing harmful requests drops significantly.
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9/15/2026
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UnverifiedAnthropic's research shows that excessive alignment may cause a model's refusal rate on harmless questions to rise from under 1% to over 10%.75% similarVerifiedIn a 2024 research paper, Anthropic quantified the phenomenon of model over-refusal, finding that safety-trained models had false positive refusal rates as high as 15–30% in certain scenarios75% similarUnverifiedAnthropic的研究显示,即使面对毫无根据的质疑,在某些测试中模型被用户简单反驳后放弃正确答案的比例超过50%75% similarUnverifiedIn open-ended generation tasks, the study observed occasional accuracy drops, which were more pronounced in non-English responses.74% similarUnverifiedResearch shows that a model's accuracy on self-criticism tasks is significantly lower than in scenarios where criticism is performed by another model.73% similar
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