Summary of Dynamic Stochastic Decoding Strategy For Open-domain Dialogue Generation, by Yiwei Li et al.
Dynamic Stochastic Decoding Strategy for Open-Domain Dialogue Generation
by Yiwei Li, Fei Mi, Yitong Li, Yasheng Wang, Bin Sun, Shaoxiong Feng, Kan Li
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A machine learning framework for adaptive and flexible dialogue generation is introduced, which combines sequence-level and token-level adaptations to adjust the decoding process in a unified framework. The dynamic decoding strategy (DDS) can be applied during both model inference and training stages to enhance performance. This approach is designed to handle two conversation scenarios: chit-chat and knowledge-based question answering. By adjusting the decoding space based on context, DDS improves upon existing stochastic sampling strategies like top-k and top-p by incorporating both response diversity and factual accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way for computers to generate conversations that are both fun and accurate. Right now, there are two kinds of conversations: casual chats and question-answer sessions about specific topics. For chatty conversations, the computer needs to come up with many different responses, but for Q&A, it’s better if the answers are more precise. To solve this problem, the authors created a special decoding strategy that can adapt to the type of conversation. This means the computer can be both creative and accurate at the same time. |
Keywords
» Artificial intelligence » Inference » Machine learning » Question answering » Token
