Retrieval-free Knowledge Injection through Multi-Document Traversal for Dialogue Models (2023.acl-long)
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Rui Wang, Jianzhu Bao, Fei Mi, Yi Chen, Hongru Wang, Yasheng Wang, Yitong Li, Lifeng Shang, Kam-Fai Wong, Ruifeng Xu
| Challenge: | Existing research on retrieval-augmented and retrieval free dialogue models focuses on retrieving knowledge from external sources and rely on finely annotated retrieval training data and knowledge-grounded responses. |
| Approach: | They propose a retrieval-free approach by turning knowledge documents into simulated multi-turn dialogues using a Multi-Document Traversal algorithm. |
| Outcome: | The proposed approach outperforms retrieval-augmented models while being cheaper and faster at domain transfer. |
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