Abstract

Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. We present ExPerT, a query-wise personalization framework that adapts LLM responses to users' query domain expertise by combining semantic and behavioral cues. ExPerT consists of two key components: (i) a semantic-behavioral expertise inference module that jointly interprets query text and keystroke dynamics via in-context LLM prompting, and (ii) an expertise-conditioned response generation that adapts the level of detail, terminology, and conceptual complexity. Our user study with 40 participants and 1270 queries demonstrated that ExPerT reduced expertise inference error by 65.7% compared to the strongest baseline (MAE = 0.398 vs. 1.162) and improved response satisfaction by 17.52% (from 3.71 to 4.36) on a 5-point Likert scale.

A person's expertise is rarely the same across every subject. Someone deeply familiar with one field may know little about another, and even within the same field, expertise can vary from topic to topic. Yet, AI systems often personalize responses using relatively fixed information, such as user profiles or previous conversations.

Led by Professor Taesik Gong of the Department of Computer Science and Engineering at UNIST, researchers developed ExPerT, an AI personalization framework that adapts large language model (LLM) responses to a user's expertise for each question. It combines what a user asks with patterns in how they type, then adjusts the detail, terminology, and complexity of the response.

Existing personalization methods can capture general preferences or background but may miss how much someone knows about a particular topic. ExPerT instead assesses expertise for each query, drawing on both the wording of the question and typing patterns such as keystroke timing, speed, and corrections. These signals are used to estimate expertise on a five-level scale, from novice to expert.

Typing behavior provided useful clues. Participants with greater expertise tended to type domain-specific terms faster and with more consistent timing. But neither language nor typing behavior was reliable on its own: an expert may ask a simple question, while someone less familiar with a subject may use technical terms fluently. Combining the two helped ExPerT distinguish between such cases.

The team evaluated ExPerT with 40 participants across chemistry, computer science, and business, collecting 1,270 queries along with typing data and self-reported expertise. Adding typing behavior to query semantics reduced the average expertise-estimation error from 0.488 to 0.398 levels, an improvement of 18.4%.

Compared with the strongest existing baseline, ExPerT reduced the error by 65.7%, from a mean absolute error of 1.162 to 0.398. Expertise-adapted responses also received higher satisfaction ratings, rising from 3.71 to 4.36 on a five-point scale, an increase of 17.52%.

“Existing personalization methods often rely on fixed profiles or previous conversations, making it difficult to reflect how much a user knows about the topic at hand,” said Professor Gong. “By adapting to expertise at the level of each question, ExPerT could support AI systems that communicate more effectively with users of different backgrounds and levels of knowledge.”

The approach could be particularly useful in areas where expertise varies widely among users, including education, professional customer support, and specialized services in medicine, law, and finance.

Yeji Park, a graduate student in UNIST's Graduate School of Artificial Intelligence and first author of the study, presented the work at ACL 2026 in San Diego. The paper was selected for an oral presentation, placing it among the top 4% of submissions, and received a SAC Highlight Award , given to the top 2%.

The dataset and source code are publicly available through theExPerT GitHub repository. The research was supported by the Ministry of Science and ICT (MSIT), the Institute of Information & Communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea (NRF), through programs supporting AI research and talent development.

Journal Reference

Yeji Park, Jiwon Tark, and Taesik Gong, "ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues," ACL '26,  (2026).