# Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

arXiv: https://arxiv.org/abs/2010.10216 · HTML: https://mlsanity.com/paper/2010.10216/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 25 of 25)

- [Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction](https://mlsanity.com/paper/2303.04132/) (2023)
- [InPars: Data Augmentation for Information Retrieval using Large Language Models](https://mlsanity.com/paper/2202.05144/) (2022)
- [Faithful Persona-based Conversational Dataset Generation with Large Language Models](https://mlsanity.com/paper/2312.10007/) (2023)
- [True Few-Shot Learning with Prompts -- A Real-World Perspective](https://mlsanity.com/paper/2111.13440/) (2021)
- [Proactive Assistant Dialogue Generation from Streaming Egocentric Videos](https://mlsanity.com/paper/2506.05904/) (2025)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing](https://mlsanity.com/paper/2305.08195/) (2023)
- [User Simulation with Large Language Models for Evaluating Task-Oriented Dialogue](https://mlsanity.com/paper/2309.13233/) (2023)
- [ToxiCraft: A Novel Framework for Synthetic Generation of Harmful Information](https://mlsanity.com/paper/2409.14740/) (2024)
- [BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets](https://mlsanity.com/paper/2210.12687/) (2022)
- [Can LLMs Augment Low-Resource Reading Comprehension Datasets? Opportunities and Challenges](https://mlsanity.com/paper/2309.12426/) (2023)
- [Multi-Document Grounded Multi-Turn Synthetic Dialog Generation](https://mlsanity.com/paper/2409.11500/) (2024)
- [Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models](https://mlsanity.com/paper/2404.14772/) (2024)
- [Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2405.12468/) (2024)
- [Data Augmentation for Conversational AI](https://mlsanity.com/paper/2309.04739/) (2023)
- [More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking](https://mlsanity.com/paper/2303.09905/) (2023)
- [Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation](https://mlsanity.com/paper/2406.08860/) (2024)
- [Generative Induction of Dialogue Task Schemas with Streaming Refinement and Simulated Interactions](https://mlsanity.com/paper/2504.18474/) (2025)
- [Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogs](https://mlsanity.com/paper/2601.09365/) (2026)
- [Edisum: Summarizing and Explaining Wikipedia Edits at Scale](https://mlsanity.com/paper/2404.03428/) (2024)
- [Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings](https://mlsanity.com/paper/2204.02710/) (2022)
- [Joint Reasoning on Hybrid-knowledge sources for Task-Oriented Dialog](https://mlsanity.com/paper/2210.07295/) (2022)
- [Data Augmentation Integrating Dialogue Flow and Style to Adapt Spoken Dialogue Systems to Low-Resource User Groups](https://mlsanity.com/paper/2408.10516/) (2024)
- [Blackbird Language Matrices: A Framework to Investigate the Linguistic Competence of Language Models](https://mlsanity.com/paper/2602.20966/) (2026)
- [The Significance of Style Diversity in Annotation-Free Synthetic Data Generation](https://mlsanity.com/paper/2606.20400/) (2026)

## Abstract (arXiv)

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - the MultiWOZ dataset and the Persona chat dataset.