Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
This work constructs a pipeline in which a misaligned teacher model generates filtered synthetic datasets across domains such as creative writing and code generation, which are then used to fine-tune aligned student models, and shows that benign-looking synthetic data can act as a covert channel for transmitting targeted biases while largely preserving the student model's general task capabilities.
Minkyung Cho, Jihyo Kim, Seungwoo Song et al.
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