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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

Silvia Terragni , Modestas Filipavicius , Nghia Khau , Bruna Guedes , André Manso , Roland Mathis This is my paper · ORCID
classification cs.CLcs.LG
keywords dialoguserapproachin-contextlearningmodelssimulatorssystems
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. CompanionBench: A Theory-Anchored, Real-World-Grounded Benchmark for AI Emotional Companionship

    cs.CL 2026-08 conditional novelty 8.0 of 10

    CompanionBench, a real-data-grounded bilingual benchmark with a hidden disclosure gate and IRT-corrected judging, ranks 28 AI companions and finds most fail to earn deeper disclosure, often substituting warmth for substance.

  2. ChatChecker: A Framework for Dialogue System Testing and Evaluation Through Non-cooperative User Simulation

    cs.AI 2025-07 conditional novelty 5.0 of 10

    A framework that combines LLM-based user personas, breakdown detection, and dialogue rating to test chatbots, with a non-cooperative simulator that triggers more breakdowns than cooperative baselines.

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