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        <title>Diving Deep into Modes of Fact Hallucinations in Dialogue Systems</title>
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        <namePart type="given">Souvik</namePart>
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    <name type="personal">
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/2022.findings-emnlp.48</identifier>
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%0 Conference Proceedings
%T Diving Deep into Modes of Fact Hallucinations in Dialogue Systems
%A Das, Souvik
%A Saha, Sougata
%A Srihari, Rohini
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F das-etal-2022-diving
%X 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.
%R 10.18653/v1/2022.findings-emnlp.48
%U https://aclanthology.org/2022.findings-emnlp.48
%U https://doi.org/10.18653/v1/2022.findings-emnlp.48
%P 684-699

Markdown (Informal)

[Diving Deep into Modes of Fact Hallucinations in Dialogue Systems](https://aclanthology.org/2022.findings-emnlp.48) (Das et al., Findings 2022)

• Diving Deep into Modes of Fact Hallucinations in Dialogue Systems (Das et al., Findings 2022)

ACL

• Souvik Das, Sougata Saha, and Rohini Srihari. 2022. Diving Deep into Modes of Fact Hallucinations in Dialogue Systems. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 684–699, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.