Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
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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.
MultEval supports collaborative creation of LLM-as-a-judge criteria by surfacing disagreements via consensus-building methods, allowing iterative revisions with examples and history, and keeping transparent how human judgments become automated rules.
CoMAP introduces a graph-based shared workspace with dual-modality AI that improves educators' project-based learning design expression, divergent thinking, and iteration over dialogue-only baselines.
A decision-theoretic model based on the observed Confirmation-Diagnosis-Correction-Redo user pattern places intermediate confirmations in AI agent tasks, yielding 81% user preference and 13.54% faster completion versus confirm-at-end.
citing papers explorer
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PersonaTeaming: Supporting Persona-Driven Red-Teaming for Generative AI
Persona-driven workflow and interface improve automated and human-AI red-teaming of generative AI by incorporating diverse perspectives into adversarial prompt creation.
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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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MultEval: Supporting Collaborative Alignment for LLM-as-a-Judge Evaluation Criteria
MultEval supports collaborative creation of LLM-as-a-judge criteria by surfacing disagreements via consensus-building methods, allowing iterative revisions with examples and history, and keeping transparent how human judgments become automated rules.
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Thinking in Graphs with CoMAP: A Shared Visual Workspace for Designing Project-Based Learning
CoMAP introduces a graph-based shared workspace with dual-modality AI that improves educators' project-based learning design expression, divergent thinking, and iteration over dialogue-only baselines.
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When Should Users Check? Modeling Confirmation Frequency inMulti-Step Agentic AI Tasks
A decision-theoretic model based on the observed Confirmation-Diagnosis-Correction-Redo user pattern places intermediate confirmations in AI agent tasks, yielding 81% user preference and 13.54% faster completion versus confirm-at-end.