Diving Deep into Modes of Fact Hallucinations in Dialogue Systems

• URL: http://arxiv.org/abs/2301.04449v1
• Date: Wed, 11 Jan 2023 13:08:57 GMT
• Title: Diving Deep into Modes of Fact Hallucinations in Dialogue Systems
• Authors: Souvik Das, Sougata Saha and Rohini K. Srihari
• Abstract summary: Knowledge Graph(KG) grounded conversations often use large pre-trained models and usually suffer from fact hallucination. We build an entity-level hallucination detection system, which would provide fine-grained signals that control fallacious content while generating responses.
• Score: 2.8360662552057323
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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