"Act Like a 5th Grader" is Not Enough: Bounding Knowledge in LLM-Based User Simulators

arXiv:2608.30033 · cs.CL, cs.AI · Submitted 2026-08-30 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-30

Updated: 2026-08-30

Code: https://github.com/iai-group/emnlp2026-simreading

Terminology

• Accuracy — 25× in this paper · explained in 1 episode
• Fidelity — 18× in this paper · explained in 2 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Calibration — 9× in this paper · explained in 7 episodes
• Persona Prompting — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Targeted Scanning — 5× in this paper
• Cognitively Bounded User Simulator — 4× in this paper
• Single-Pass Reading — 4× in this paper
• Jensen-Shannon Divergence — 3× in this paper
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Expected Calibration Error — 2× in this paper
• Grounding — 2× in this paper · explained in 3 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adaptive Decoding — 1× in this paper · explained in 1 episode
• Agentic Evaluation — 1× in this paper · explained in 1 episode
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Cognitive Load — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Ensemble Approach — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Synthetic Data Generation — 1× in this paper · explained in 5 episodes

Sources

• LLM-based Cognitive Models of Students with Misconceptions
• In-Context Learning User Simulators for Task-Oriented Dialog Systems
• EduAgent: Generative Student Agents in Learning

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