Existing user simulation approaches focus on generating user-like responses in dialogue. They often assume that the provided persona is sufficient for producing such responses, without verifying whether critical personas are supplied. This raises concerns about the validity of simulation results.To address this issue, we study the task of identifying persona dimensions (e.g., ”whether the user is price-sensitive”) that are relevant but missing in simulating a user’s reply for a given dialogue context.We introduce PICQ-drama (constructed from TVShowGuess), a benchmark of context-aware choice questions, annotated with missing persona dimensions whose absence leads to ambiguous user choices. We further design diverse evaluation criteria for missing persona identification.Benchmarking leading LLMs on our PICQ-drama dataset demonstrates the feasibility of this task. Evaluation across diverse criteria, along with further analyses, reveals cognitive differences between LLMs and humans and highlights the distinct roles of different persona categories in shaping responses.

Large language models (LLMs) enable simulating individual responses from persona information, supporting applications such as opinion elicitation and virtual character creation. However, existing approaches typically assume rich persona profiles, which are often unavailable in practice. In this work, motivated by recent findings that LLMs can identify query-relevant persona dimensions (e.g., whether a user is price-sensitive), we study query-focused individual simulation under cold-start settings, where relevant persona information is identified and requested on demand for each query. To solve this task while minimizing the number of persona requests, we explore a progressive method that iteratively predicts the most critical relevant persona dimension and uses self-reported confidence as a stopping signal to determine when sufficient information has been collected. Experiments on two dialogue datasets show that this query-driven paradigm achieves simulation performance comparable to approaches that rely on rich persona information extracted from dialogue history, using only a few persona dimensions (up to five per query), and this number is further reduced by our progressive method while maintaining or improving simulation quality.