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Reliable LLMbased User Simulator for TaskOriented Dialogue Systems Ivan Sekulic, Silvia Terragni, Victor Guimares, Nghia Khau, Bruna Guedes, Modestas Filipavicius, Andr Ferreira Manso, Roland Mathis Telepathy Labs GmbH Zrich, Switzerland firstname.lastname@telepathy.aiAbstractsimulation methods require a substantial amount of annotated data (Lin et al., 2021, 2022, 2023), or a deep understanding of the internal workings of the dialogue system they interact with (Schatzmann et al., 2007; Li et al
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How to fill out reliable llm-based user simulator

01
Identify the target user group for simulation.
02
Gather relevant data on user behavior and preferences.
03
Define the specific scenarios and tasks to be simulated.
04
Choose an appropriate LLM (large language model) framework or platform.
05
Configure the model parameters to reflect realistic user interactions.
06
Input the gathered data into the simulator.
07
Test the simulator with sample interactions to ensure reliability.
08
Iterate on the model based on feedback and performance results.

Who needs reliable llm-based user simulator?

01
Developers of conversational agents and chatbots.
02
Researchers in artificial intelligence and user experience.
03
Companies looking to improve customer service automation.
04
Educational institutions for training and evaluation purposes.
05
Product teams seeking to refine user interfaces through testing.
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A reliable llm-based user simulator is a tool or software that utilizes large language models (LLMs) to imitate user behavior and interactions for testing, training, or research purposes.
Individuals or organizations that are utilizing the llm-based user simulator for application testing, product development, or research purposes are typically required to file it.
Filling out a reliable llm-based user simulator generally involves specifying the intended use case, configuring user profiles, and outlining the scenarios or tasks that the simulator should execute.
The purpose of a reliable llm-based user simulator is to provide a controlled environment to evaluate user interactions, improve system responsiveness, or enhance product design based on simulated user behavior.
Information that must be reported on a reliable llm-based user simulator includes configuration details, user scenario descriptions, performance metrics, and any issues encountered during simulation.
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