Summary of Inductive-deductive Strategy Reuse For Multi-turn Instructional Dialogues, by Jiao Ou et al.
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
by Jiao Ou, Jiayu Wu, Che Liu, Fuzheng Zhang, Di Zhang, Kun Gai
First submitted to arxiv on: 17 Apr 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 This research paper proposes a novel approach to generating high-quality instructional dialogues for large language models (LLMs) by explicitly capturing complex rules that govern instruction posing. The authors introduce a method that first induces high-level instruction strategies from real-world dialogue scenarios, and then applies these strategies deductively to pose diverse and in-depth instructions. Experimental results demonstrate the effectiveness of this approach, with constructed multi-turn dialogues outperforming competitive baselines on downstream chat models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers better understand how people ask questions by giving them rules that are more like how humans think. Right now, computers have trouble asking good questions without being told exactly what to say. This makes it hard for them to learn from conversations. The scientists in this study found a way to teach computers these rules by looking at real conversations where someone is giving instructions. They used these rules to come up with new ways that the computer could ask questions, and it did better than other methods. |
