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In real life, helpful and malicious feedback are often mixed together to feed the model.
And malicious feedback ratings are detected by adopting cumulative sum method.
Following a similar methodology as the previous experiments, such malicious feedback was provided for four rounds.
It could be used to address situations where malicious feedback has been received but subsequent helpful feedback is not available.
Their reputation measure has three phases (i.e., feedback checking, feedback adjustment, and malicious feedback detection) to enhance the accuracy.
The model is also shown to be robust with respect to malicious feedback, quickly recovering based on helpful user feedback.
Similar(49)
Based on this framework, we proposed an approach for filtering out malicious feedbacks and a trust metric to evaluate the trustworthiness of service provider.
Figure 11 System accuracy, providing malicious user feedback, followed by knowledgeable and helpful user feedback.
Providing malicious user feedback, followed by knowledgeable and helpful user feedback illustrates the ability of the model to self-recover.
Providing malicious user feedback, followed by knowledgeable and helpful user feedback also recovers the system precision to a normal level.
Figure 12 System precision, providing malicious user feedback.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com