User Model Extraction via Belief Self-Distillation

Ali Holmov, Yiran Huang, Kirill Bykov et al.1 min readarXivSep 25, 2026

Key Takeaway for Students

Large language models (LLMs) implicitly infer attributes of their users and adapt their behavior accordingly, yet these beliefs remain difficult to inspect and causally manipulate. We introduce Belief Self-Distillation (BSD), a unified read-write framework that bridges linear and causal probing by learning a compact user representation that can be both decoded and written back

Reading in Plain English mode — toggle below to switch

Understanding and controlling how LLMs form beliefs about users is crucial for developing safer, more personalized, and less biased AI systems. This research offers a novel framework for both extracting and manipulating these implicit user models, moving beyond simple probing to causal intervention.

Read original on arXiv

Career Insight

medium demand

Why it matters

Skills to learn

Causal inference techniques in MLProbing methods for LLM activationsImplementing distillation techniques for LLMsDesigning experiments for LLM interpretabilityDeveloping user-adaptive LLM applications

Interview questions

Explain the difference between linear probing and causal probing in the context of LLM interpretability, and how Belief Self-Distillation bridges this gap.
How would you evaluate the fidelity of a user belief representation extracted using BSD, and what are the potential challenges in ensuring its causal role?
Design a system architecture for an LLM-powered assistant that dynamically adapts its behavior based on a user model extracted and updated in real-time using a framework like BSD, considering privacy and computational constraints.

Relevant for ~18 months · generated by the enrichment pipeline