Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls. (arXiv:2208.03295v1 [cs.CL])

The promise of interaction between intelligent conversational agents and
humans is that models can learn from such feedback in order to improve.
Unfortunately, such exchanges in the wild will not always involve human
utterances that are benign or of high quality, and will include a mixture of
engaged (helpers) and unengaged or even malicious users (trolls). In this work
we study how to perform robust learning in such an environment. We introduce a
benchmark evaluation, SafetyMix, which can evaluate methods …

arxiv case data helpers learning mixed trolls