Spinning Language Models: Risks of Propaganda-As-A-Service and Countermeasures

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to "spin" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a sp...

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Veröffentlicht in:	arXiv.org 2022-04
Hauptverfasser:	Bagdasaryan, Eugene, Shmatikov, Vitaly
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computation and Language Computer Science - Cryptography and Security Computer Science - Learning Propaganda Speech recognition Supply chains Toxicity Training
Online-Zugang:	Volltext
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