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Knowledge Graph(KG) grounded conversations often use large pre-trained modelsand usually suffer from fact hallucination. Frequently entities with noreferences in knowledge sources and conversation history are introduced intoresponses, thus hindering the flow of the conversation -- existing work attemptto overcome this issue by tweaking the training procedure or using a multi-steprefining method. However, minimal effort is put into constructing anentity-level hallucination detection system, which would provide fine-grainedsignals that control fallacious content while generating responses. As a firststep to address this issue, we dive deep to identify various modes ofhallucination in KG-grounded chatbots through human feedback analysis.Secondly, we propose a series of perturbation strategies to create a syntheticdataset named FADE (FActual Dialogue Hallucination DEtection Dataset). Finally,we conduct comprehensive data analyses and create multiple baseline models forhallucination detection to compare against human-verified data and alreadyestablished benchmarks.

Diving Deep into Modes of Fact Hallucinations in Dialogue Systems