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About this paper
Zihan Liu, Mostofa Patwary, Ryan Prenger, Shrimai Prabhumoye, Wei Ping, Mohammad Shoeybi, Bryan Catanzaro
The Multi-Stage Dialogue Prompting (MSDP) framework generates informative and engaging dialogue responses using pre-trained language models in a two-stage process, eliminating the need for fine-tuning or external knowledge retrieval. This approach demonstrates improved knowledgeability and engagement in responses compared to traditional methods, while also generalizing better to out-of-domain topics.
Key insights
- Large language models can serve as internal knowledge sources for dialogue without explicit external retrieval or fine-tuning, through careful prompt engineering.
- A two-stage prompting approach, separating knowledge generation from dialogue response generation, effectively structures the task for in-context learning.
- Careful sample selection and prompt construction are crucial for guiding the language model to generate relevant knowledge and integrate it naturally into responses.
Summary by alphaXiv.
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