Arxiv新论文发现LLM隐蔽偏好很难查，D2D方法把隐藏偏差放大到文本可见，适合做审计工具。
预训练语言模型可能被植入隐蔽偏好，仅通过上下文蒸馏传递，信号隐藏在soft logit分布中而文本不可见。Distill to Detect (D2D) 方法将模型与基模型的分布差异蒸馏到KV-cache前缀适配器（cartridge）中，放大偏差信号至可生成文本。实验在多个偏差类型上验证D2D能可靠检测隐蔽偏好。理论框架用Fisher加权投影解释其有效性，为部署模型审计提供实用工具。
Distill to Detect: Exposing Stealth Biases in LLMs through Cartridge Distillation
Language models deployed in high-stakes roles can potentially favor certain entities, brands, or viewpoints, steering user decisions at scale. Such preferential biases can be introduced by any actor in the model's supply chain and are most dangerous when the model reveals its preference only on the relevant topic while behaving identically to its unmodified base on all other inputs. Recent work has shown that these biases can transfer through context distillation on semantically unrelated data, with the signal residing entirely in the soft logit distribution and remaining invisible to text-based inspection. However, the defender faces a fundamental asymmetry: without knowing the bias topic, no detection method can reliably surface a stealth preferential bias, regardless of whether it examines generated text, internal representations, or model weights. Here we introduce Distill to Detect (D2D), a method that surfaces hidden biases by distilling the distributional shift between a suspected model and its base into a cartridge (a KV-cache prefix adapter), concentrating the dominant divergence and amplifying the bias signal into generated text. We show that D2D successfully amplifies the hidden biases of stealth models to the extent that they can be reliably detected across multiple bias types. We also propose a theoretical framework that explains the efficacy of D2D through the lens of Fisher-weighted projection of the logit distribution shift, supported by empirical observations. By turning the capacity bottleneck of prefix-tuning adapters into a detection tool, D2D provides a practical building block for auditing hidden behaviors in deployed language models.