ConvApparel is a human-AI conversation dataset and evaluation framework designed to quantify and reduce the realism gap in LLM-based user simulators. Using dual-agent shopping dialogues and three validation pillars—statistical alignment, human-likeness scoring, and counterfactual tests—researchers show that data-driven simulators adapt more like real users but still exhibit detectable artifacts.

• Google researchers built ConvApparel by secretly routing users to a "Good" or "Bad" recommender across 4,000+ sessions and 15,000 turns, capturing real turn-by-turn satisfaction/frustration ratings.
• They tested prompting, in-context learning, and fine-tuning (on Gemini 2.5 Flash) as user simulators, validating each against stats alignment, a human-likeness discriminator, and counterfactual "Good vs Bad" agent tests.
• Prompt-based and in-context simulators failed to show genuine frustration when switched to the "Bad" agent, staying too smooth and helpful.
• Only the fine-tuned simulator closely matched real humans' shifts in frustration and satisfaction, showing targeted training on real interactions beats prompting or few-shot examples for realistic simulation.