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In-Context Learning User Simulators for Task-Oriented Dialog Systems

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arxiv 2306.00774 v1 pith:UJ65XUO6 submitted 2023-06-01 cs.CL cs.LG

In-Context Learning User Simulators for Task-Oriented Dialog Systems

Silvia Terragni , Modestas Filipavicius , Nghia Khau , Bruna Guedes , Andr\'e Manso , Roland Mathis This is my paper
classification cs.CL cs.LG
keywords dialoguserapproachin-contextlearningmodelssimulatorssystems
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper presents a novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach. By harnessing the power of these models, the proposed approach generates diverse utterances based on user goals and limited dialog examples. Unlike traditional simulators, this method eliminates the need for labor-intensive rule definition or extensive annotated data, making it more efficient and accessible. Additionally, an error analysis of the interaction between the user simulator and dialog system uncovers common mistakes, providing valuable insights into areas that require improvement. Our implementation is available at https://github.com/telepathylabsai/prompt-based-user-simulator.

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Cited by 2 Pith papers

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  2. Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants

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    Fine-tuned simulators grounded in real human data produce LLM assistants that win more often against real users than those trained against role-playing simulators.