Diving Deep into Modes of Fact Hallucinations in Dialogue Systems (2022.findings-emnlp)
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| Challenge: | Knowledge Graph(KG) grounded conversations often use large pre-trained models and suffer from fact hallucination. |
| Approach: | They propose to use a human feedback analysis to identify various modes of hallucination in KG chatbots. |
| Outcome: | The proposed system provides fine-grained signals that control fallacious content while generating responses. |
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| Challenge: | Existing knowledge-grounded conversational benchmarks produce factually invalid statements, a phenomenon commonly called hallucination. |
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| Challenge: | Dialogue systems that generate factually incorrect responses are often unfitful and hallucinate factuality invalid. |
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| Challenge: | Existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations, but they are plagued by the Knowledge Hallucination problem. |
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| Challenge: | Existing knowledge-grounded dialogue systems generate accurate and informative responses, but they are prone to hallucination problems. |
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| Challenge: | State-of-the-art dialogue models suffer from factual incorrectness and hallucination of knowledge. |
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| Challenge: | Existing methods for hallucination detection are coarse-grained and lack long-range consistency checks. |
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Che Jiang, Biqing Qi, Xiangyu Hong, Dayuan Fu, Yang Cheng, Fandong Meng, Mo Yu, Bowen Zhou, Jie Zhou
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| Challenge: | Existing knowledge-grounded dialogue generation models face the hallucination problem . Existing models generate inappropriate knowledge and generate inconsistent responses . |
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| Challenge: | State-of-the-art abstractive summarization systems often generate hallucinations, i.e., content that is not directly inferable from the source document. |
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The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
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