Validity of LLM-simulated users as proxies for real users

Determine whether interactions between large language model agents and LLM-simulated users accurately reflect and predict interactions between those agents and real human users in agentic evaluations, in order to establish the validity of using LLM-simulated users for benchmarking multi-turn, tool-using conversational agents.

Background

Agentic benchmarks often replace human participants with LLM-simulated users to enable scalable, automated evaluation of multi-turn, tool-using conversational agents. However, without direct validation against human interactions, there is a risk that outcomes measured with simulated users do not generalize to real users, potentially leading to miscalibrated assessments of agent capabilities.

This paper studies this concern using τ-Bench retail tasks and a cross-national user study, introducing a Human–LLM calibration metric and documenting systematic miscalibration across difficulty levels and demographic groups—motivating the need to rigorously determine whether simulated interactions reliably predict real human outcomes.