# Bottom-Up Synthesis of Knowledge-Grounded Task-Oriented Dialogues with Iteratively Self-Refined Prompts

arXiv: https://arxiv.org/abs/2504.14375 · HTML: https://mlsanity.com/paper/2504.14375/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration](https://mlsanity.com/paper/2402.00367/) (2024)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://mlsanity.com/paper/2212.10465/) (2022)
- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [PLACES: Prompting Language Models for Social Conversation Synthesis](https://mlsanity.com/paper/2302.03269/) (2023)
- [Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs](https://mlsanity.com/paper/2403.05020/) (2024)
- [Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training](https://mlsanity.com/paper/2406.00222/) (2024)
- [Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs](https://mlsanity.com/paper/2310.11689/) (2023)
- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [Domain Adaptive Dialog Generation via Meta Learning](https://mlsanity.com/paper/1906.03520/) (2019)
- [Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding](https://mlsanity.com/paper/2210.14169/) (2022)
- [NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation](https://mlsanity.com/paper/2310.14563/) (2023)
- [Zero-Shot Dialog Generation with Cross-Domain Latent Actions](https://mlsanity.com/paper/1805.04803/) (2018)
- [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://mlsanity.com/paper/2307.10172/) (2023)
- [LLaMA Rider: Spurring Large Language Models to Explore the Open World](https://mlsanity.com/paper/2310.08922/) (2023)
- [MG-ShopDial: A Multi-Goal Conversational Dataset for e-Commerce](https://mlsanity.com/paper/2304.12636/) (2023)
- [SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues](https://mlsanity.com/paper/2204.10591/) (2022)
- [Universal Self-Adaptive Prompting](https://mlsanity.com/paper/2305.14926/) (2023)

## Built on by (most cited first, 5 of 5)

- [MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes](https://mlsanity.com/paper/2605.06897/) (2026)
- [PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding](https://mlsanity.com/paper/2506.17310/) (2025)
- [CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics](https://mlsanity.com/paper/2604.15647/) (2026)
- [BridgeAlign: Bridging Preference Alignment for Humanities and Social Sciences](https://mlsanity.com/paper/2607.27366/) (2026)
- [HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs](https://mlsanity.com/paper/2607.27379/) (2026)

## Abstract (arXiv)

Training conversational question-answering (QA) systems requires a substantial amount of in-domain data, which is often scarce in practice. A common solution to this challenge is to generate synthetic data. Traditional methods typically follow a top-down approach, where a large language model (LLM) generates multi-turn dialogues from a broad prompt. Although this method produces coherent conversations, it offers limited fine-grained control over the content and is susceptible to hallucinations. We introduce a bottom-up conversation synthesis approach, where QA pairs are generated first and then combined into a coherent dialogue. This method offers greater control and precision by dividing the process into two distinct steps, allowing refined instructions and validations to be handled separately. Additionally, this structure allows the use of non-local models in stages that do not involve proprietary knowledge, enhancing the overall quality of the generated data. Both human and automated evaluations demonstrate that our approach produces more realistic and higher-quality dialogues compared to top-down methods.