Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features (2021.acl-long)
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| Challenge: | Existing systems that strive to be informative teachers are difficult to build . knowledge grounded dialogue systems are difficult because of limited training objectives . |
| Approach: | They propose to train a generative neural dialogue model that is controlled to stay faithful to evidence . they propose to use additional inputs to generate more objective responses . |
| Outcome: | The proposed model produces responses that are perceived by humans to be objective and faithful to evidence. |
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
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| Challenge: | Existing evaluation methods for factual consistency in knowledge-grounded dialogues are unreliable and limit their applicability. |
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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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Gunsoo Han, Daejin Jo, Daniel Nam, Eunseop Yoon, Taehwan Kwon, Seungeun Rho, Kyoung-Woon On, Chang Yoo, Sungwoong Kim
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| Challenge: | Recent document-grounded dialog systems have seen an increase in popularity. |
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A Synthetic Data Generation Framework for Grounded Dialogues (2023.acl-long)
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| Challenge: | Existing approaches to train grounded dialogues require large amounts of data. |
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Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment (2023.findings-emnlp)
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Boyang Xue, Weichao Wang, Hongru Wang, Fei Mi, Rui Wang, Yasheng Wang, Lifeng Shang, Xin Jiang, Qun Liu, Kam-Fai Wong
| Challenge: | Experimental results show that pretrained language models generate inconsistent factual knowledge in many conversational tasks. |
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