Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models

• URL: http://arxiv.org/abs/2404.14772v1
• Date: Tue, 23 Apr 2024 06:23:34 GMT
• Title: Simulating Task-Oriented Dialogues with State Transition Graphs and Large Language Models
• Authors: Chris Samarinas, Pracha Promthaw, Atharva Nijasure, Hansi Zeng, Julian Killingback, Hamed Zamani,
• Abstract summary: SynTOD is a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) systems. It generates diverse, structured conversations through random walks and response simulation using large language models. In our experiments, using graph-guided response simulations leads to significant improvements in intent classification, slot filling and response relevance.
• Score: 16.94819621353007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores SynTOD, a new synthetic data generation approach for developing end-to-end Task-Oriented Dialogue (TOD) Systems capable of handling complex tasks such as intent classification, slot filling, conversational question-answering, and retrieval-augmented response generation, without relying on crowdsourcing or real-world data. SynTOD utilizes a state transition graph to define the desired behavior of a TOD system and generates diverse, structured conversations through random walks and response simulation using large language models (LLMs). In our experiments, using graph-guided response simulations leads to significant improvements in intent classification, slot filling and response relevance compared to naive single-prompt simulated conversations. We also investigate the end-to-end TOD effectiveness of different base and instruction-tuned LLMs, with and without the constructed synthetic conversations. Finally, we explore how various LLMs can evaluate responses in a TOD system and how well they are correlated with human judgments. Our findings pave the path towards quick development and evaluation of domain-specific TOD systems. We release our datasets, models, and code for research purposes.

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