KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matching content to a user's evolving understanding and cognitive capacity. Yet user simulators used to evaluate and train LLMs do not explicitly model user knowledge so they neither produce realistic interactions across knowledge levels nor reflect how interactions unfold as that knowledge evolves. To close this gap, we introduce KNOWSIM, an evaluation framework built around a user simulator that maintains explicit knowledge states, represented as a graph of Information Units with prerequisite relationships, that evolve under update rules grounded in lear...
| Authors | Yoonjoo Lee, Hyoungwook Jin, Tae Soo Kim et al. |
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| Primary category | cs.AI |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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