Summary of Transfertod: a Generalizable Chinese Multi-domain Task-oriented Dialogue System with Transfer Capabilities, by Ming Zhang et al.
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer Capabilities
by Ming Zhang, Caishuang Huang, Yilong Wu, Shichun Liu, Huiyuan Zheng, Yurui Dong, Yujiong Shen, Shihan Dou, Jun Zhao, Junjie Ye, Qi Zhang, Tao Gui, Xuanjing Huang
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study focuses on improving the efficiency, effectiveness, and proactiveness of task-oriented dialogue (TOD) systems for information collection. Building upon recent advancements in Large Language Models (LLMs), the authors present a novel dataset called TransferTOD, designed to simulate human-computer dialogues in 30 diverse life service scenarios. The dataset is generated through a multi-domain process and fine-tuned using full-parameter fine-tuning. The resulting model, TransferTOD-7B, demonstrates notable capabilities in slot filling and questioning, showcasing strong generalization abilities in various downstream scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to create more effective task-oriented dialogue systems that can collect information efficiently. To do this, the researchers created a large dataset of conversations that mimic how humans talk to computers. They then used this data to train a special kind of AI model called TransferTOD-7B. This model is very good at understanding and responding to questions, and it’s able to generalize its knowledge to new situations. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
