# ProKG-Dial: Progressive Multi-Turn Dialogue Construction with Domain Knowledge Graphs

arXiv: https://arxiv.org/abs/2508.01869 · HTML: https://mlsanity.com/paper/2508.01869/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 24 of 24)

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- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://mlsanity.com/paper/2406.15126/) (2024)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://mlsanity.com/paper/2402.18013/) (2024)
- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement](https://mlsanity.com/paper/2403.15042/) (2024)
- [MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation](https://mlsanity.com/paper/2211.05719/) (2022)
- [SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support](https://mlsanity.com/paper/2305.00450/) (2023)
- [LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPT](https://mlsanity.com/paper/2307.06917/) (2023)
- [Synthetic Dialogue Dataset Generation using LLM Agents](https://mlsanity.com/paper/2401.17461/) (2024)
- [A Survey on Data Augmentation in Large Model Era](https://mlsanity.com/paper/2401.15422/) (2024)
- [Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models](https://mlsanity.com/paper/2311.00287/) (2023)
- [MedSyn: LLM-based Synthetic Medical Text Generation Framework](https://mlsanity.com/paper/2408.02056/) (2024)
- [An Auto-Encoder Matching Model for Learning Utterance-Level Semantic Dependency in Dialogue Generation](https://mlsanity.com/paper/1808.08795/) (2018)
- [RefGPT: Dialogue Generation of GPT, by GPT, and for GPT](https://mlsanity.com/paper/2305.14994/) (2023)
- [LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues](https://mlsanity.com/paper/2403.00462/) (2024)
- [Construction of Paired Knowledge Graph-Text Datasets Informed by Cyclic Evaluation](https://mlsanity.com/paper/2309.11669/) (2023)
- [Artificial Intelligence Studies in Cartography: A Review and Synthesis of Methods, Applications, and Ethics](https://mlsanity.com/paper/2312.07901/) (2023)
- [Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media](https://mlsanity.com/paper/2406.00969/) (2024)

## Abstract (arXiv)

Current large language models (LLMs) excel at general NLP tasks but often lack domain specific precision in professional settings. Building a high quality domain specific multi turn dialogue dataset is essential for developing specialized conversational systems. However, existing methods such as manual annotation, simulated human LLM interactions, and role based LLM dialogues are resource intensive or suffer from limitations in dialogue quality and domain coverage. To address these challenges, we introduce ProKG Dial, a progressive framework for constructing knowledge intensive multi turn dialogue datasets using domain specific knowledge graphs (KGs). ProKG Dial leverages the structured nature of KGs to encode complex domain knowledge and relationships, providing a solid foundation for generating meaningful and coherent dialogues. Specifically, ProKG Dial begins by applying community detection to partition the KG into semantically cohesive subgraphs. For each subgraph, the framework incrementally generates a series of questions and answers centered around a target entity, ensuring relevance and coverage. A rigorous filtering step is employed to maintain high dialogue quality. We validate ProKG Dial on a medical knowledge graph by evaluating the generated dialogues in terms of diversity, semantic coherence, and entity coverage. Furthermore, we fine tune a base LLM on the resulting dataset and benchmark it against several baselines. Both automatic metrics and human evaluations demonstrate that ProKG Dial substantially improves dialogue quality and domain specific performance, highlighting its effectiveness and practical utility.