This study examines whether individual text corpora (ICs) from search histories can be used to simulate individual knowledge. We collected ICs from 316 adults, who answered 36 multiple-choice knowledge items, and compared several large language models (LLMs) on this task, of which only Qwen3-1.7B proved viable. After task-specific fine-tuning via Low-Rank Adaptation (LoRA), Qwen3-1.7B outperformed both participants and a representative German norm sample on publicly available items. On non-public questions, however, the LLM performed worse than our participants, suggesting possible training data contamination for the public questions. When integrating ICs into retrieval-augmented generation to predict individual responses, LLM-participant Match accuracies significantly exceeded chance, which demonstrates a detectable individual knowledge signal. The probabilities assigned to the participants' answers were, however, low and far below the probability of correct answers, indicating poor calibration toward individual response patterns. Knowledge-gap prediction was sub-optimal, though it improved for corpora exceeding five million tokens. We discuss our entropy based evaluation benchmarks as calibration indices for individualized knowledge simulation.

翻译：暂无翻译