AutoSim: A Self-Improving User Simulator for Conversational Search Evaluation

Abstract

User simulators promise to evaluate conversational search systems at a scale human studies cannot reach, but only if their behavior is realistic enough to trust. We study AutoSim, a conversational-search user simulator whose relevance judgments rank a controlled pool of retrieval systems in agreement with NIST across three TREC iKAT years (Kendall's $τ$ of $0.66$, $0.91$, and $0.91$; all $p{\le}0.002$), an agreement that survives removing its persona. AutoSim self-improves against a held-out signal from real users, and we find that fine-tuning its weights makes it behave like real users, which in turn gives a more accurate estimate of how well a system serves them. Reaching that behavior takes more than prompting: when discriminating between real and simulated user sessions, no prompt we tried lowers a held-out real-versus-simulated classifier below AUC $0.85$, whereas fine-tuning the weights drives it to $0.45$, where neither the classifier nor a human reading full dialogues can tell it from a real user. That added realism is what makes the evaluation more accurate: where real users retrieve little, a prompted simulator over-estimates retrieval effectiveness by $+0.136$ nDCG and returns a relevant passage on $57\%$ of the turns where real users find none, enough to change a deployment decision. Only the weight-level simulator escapes this bias. We release the framework and code.