12 papers
Eyes Can't Always Tell: Fusing Eye Tracking and User Priors for User Modeling under AI Advice Conditions
Xin Sun, Shu Wei, Ting Pan +5
Modeling users' cognitive states (e.g., cognitive load and decision confidence) is essential for building adaptive AI in high-stakes decision-making. While eye tracking provides no…
Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layers
Xin Sun, Yue Su, Yifan Mo +9
Language-based AI is increasingly deployed for mental health support, yet trust is evaluated in interdisciplinary but operationally misaligned ways: NLP and AI work measures robust…
Seeing the Reasoning: How LLM Rationales Influence User Trust and Decision-Making in Factual Verification Tasks
Xin Sun, Shu Wei, Jos A Bosch +3
Large Language Models (LLMs) increasingly show reasoning rationales alongside their answers, turning "reasoning" into a user-interface element. While step-by-step rationales are ty…
Large Language Models as Modal Models in Linguistics
Haruto Suzuki, Saku Sugawara
The rapid advancement of large language models (LLMs) has intensified debates about their significance for linguistic theory. These debates are commonly divided into three position…
A Dual-Task Paradigm to Investigate Sentence Comprehension Strategies in Language Models
Rei Emura, Saku Sugawara
Language models (LMs) behave more like humans when their cognitive resources are restricted, particularly in predicting sentence processing costs such as reading times. However, it…
C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
Akira Kawabata, Saku Sugawara
Rubric-augmented verification guides reward models with explicit evaluation criteria, yielding more reliable judgments than single-model verification. However, most existing method…