KnowSim: A New Framework for Evaluating Information Calibration in LLM Assistants
Researchers have introduced KnowSim, an evaluation framework designed to measure how well large language model assistants calibrate information to users' evolving knowledge. Unlike traditional user simulators, KnowSim maintains an explicit knowledge state modeled as a graph of information units with prerequisite relationships, updated according to learning theory. It generates three metrics—knowledge gain, delivery calibration, and cognitive overload—derived from the trajectory of these states. The framework was validated using 705 human-AI interaction sessions. The paper is posted on arXiv with identifier 2608.17150.
Key facts
- KnowSim is an evaluation framework for LLM assistants.
- It focuses on information calibration to user knowledge.
- The user simulator maintains explicit knowledge states.
- Knowledge states are represented as a graph of Information Units with prerequisite relationships.
- Update rules are grounded in learning theory.
- It computes three metrics: Knowledge Gain, Delivery Calibration, and Cognitive Overload.
- Validated against 705 human-AI interaction sessions.
- Paper available on arXiv:2608.17150.
Entities
Institutions
- arXiv