  Knowledge Graph(KG) grounded conversations often use large pre-trained models
and usually suffer from fact hallucination. Frequently entities with no
references in knowledge sources and conversation history are introduced into
responses, thus hindering the flow of the conversation -- existing work attempt
to overcome this issue by tweaking the training procedure or using a multi-step
refining method. However, minimal effort is put into constructing an
entity-level hallucination detection system, which would provide fine-grained
signals that control fallacious content while generating responses. As a first
step to address this issue, we dive deep to identify various modes of
hallucination in KG-grounded chatbots through human feedback analysis.
Secondly, we propose a series of perturbation strategies to create a synthetic
dataset named FADE (FActual Dialogue Hallucination DEtection Dataset). Finally,
we conduct comprehensive data analyses and create multiple baseline models for
hallucination detection to compare against human-verified data and already
established benchmarks.
