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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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.
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.
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The Case for Model Science: Verify, Explore, Steer, Refine
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.