Summary of Dflow: Diverse Dialogue Flow Simulation with Large Language Models, by Wanyu Du et al.
DFlow: Diverse Dialogue Flow Simulation with Large Language Models
by Wanyu Du, Song Feng, James Gung, Lijia Sun, Yi Zhang, Saab Mansour, Yanjun Qi
First submitted to arxiv on: 18 Oct 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 The proposed method aims to enhance the diversity of synthetic dialogues by focusing on task execution logic in developing language model-based dialogue agents. The existing data simulation methods mainly focus on increasing diversity at the utterance level, neglecting the critical aspect of task logic diversity at the dialogue level. To address this, the authors introduce a novel data simulation method that uses large language models (LLMs) to generate decision tree-structured task plans, enabling the derivation of diverse dialogue trajectories for a given task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach is introduced to develop language model-based dialogue agents by enhancing the diversity of synthetic dialogues. The existing methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, but neglect the critical aspect of task logic diversity at the dialogue level. A novel method uses LLMs to generate decision tree-structured task plans, allowing for diverse dialogue trajectories and multi-turn dialogues that follow unique paths. |
Keywords
» Artificial intelligence » Decision tree » Language model
