KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn
A new framework for measuring how AI models adapt to user knowledge levels during complex tasks
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...
ArXiv CS.AI
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KnowSim transforms LLM evaluation by replacing static benchmarks with dynamic simulators that track how a user's knowledge evolves during a conversation. This breakthrough reveals crucial aptitude-treatment interactions, ensuring AI assistants can effectively tailor their explanations to match a user's specific cognitive capacity.
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