A unified detection and unlearning framework identifies and mitigates data poisoning in summarization models, achieving 85-92% detection and up to 96% behavior restoration across multiple architectures.
Countering the effects of lead bias in news summarization via multi-stage training and auxiliary losses.arXiv preprint arXiv:1909.04028, 2019
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Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
A unified detection and unlearning framework identifies and mitigates data poisoning in summarization models, achieving 85-92% detection and up to 96% behavior restoration across multiple architectures.