Evalet applies functional fragmentation to deliver fragment-level qualitative analysis of LLM evaluations, with a user study showing 48% more misalignment detections than holistic scoring.
Proceedings of the 28th International Conference on Intelligent User Interfaces , pages =
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A survey of 457 papers yields a six-dimensional design space for abstraction in interactive systems that reframes gulfs of execution and evaluation while articulating cognitive and design processes for bridging abstraction gaps.
ExPerT infers query-specific user expertise from semantic text and keystroke dynamics via LLM prompting to adapt response generation, cutting inference error 65.7% and raising satisfaction 17.52% in a 40-participant study.
An LLM-in-the-loop study with 17 interviewers identifies five ethical concerns with AI-generated follow-up questions and translates them into design and governance implications.
User study finds that task difficulty affects keystroke dynamics during LLM prompting as a marker of cognitive effort, while device type has weaker effects and keystrokes do not predict perceived output usefulness.
Position paper proposing Model Science as a discipline to systematically analyze AI model behavior beyond benchmarks, drawing analogies from cognitive science, neuroscience, medicine, and agriculture.
citing papers explorer
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Evalet: Evaluating Large Language Models through Functional Fragmentation
Evalet applies functional fragmentation to deliver fragment-level qualitative analysis of LLM evaluations, with a user study showing 48% more misalignment detections than holistic scoring.
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Making Abstraction Concrete: A Design Space and Interaction Model of Abstraction in Interactive Systems
A survey of 457 papers yields a six-dimensional design space for abstraction in interactive systems that reframes gulfs of execution and evaluation while articulating cognitive and design processes for bridging abstraction gaps.
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ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues
ExPerT infers query-specific user expertise from semantic text and keystroke dynamics via LLM prompting to adapt response generation, cutting inference error 65.7% and raising satisfaction 17.52% in a 40-participant study.
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Ethics and Social Responsibility in AI-Assisted Interviewing: An LLM-in-the-Loop Study of AI-Generated Follow-Up Questions
An LLM-in-the-loop study with 17 interviewers identifies five ethical concerns with AI-generated follow-up questions and translates them into design and governance implications.
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Typing Behavior in Human-LLM Interaction: Keystroke Dynamics Reveal Cognitive Effort During Prompting
User study finds that task difficulty affects keystroke dynamics during LLM prompting as a marker of cognitive effort, while device type has weaker effects and keystrokes do not predict perceived output usefulness.