Rationalize proposes a role-pair framework for shared semantic reasoning to support bidirectional human-AI alignment through explicit rationalization of intent.
Proceedings of the 29th International Conference on Intelligent User Interfaces , pages =
9 Pith papers cite this work, alongside 18 external citations. Polarity classification is still indexing.
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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 AI agent representing outgroup views in ingroup chat directionally reduced intergroup anxiety and improved perspective-taking versus passive document exposure.
An experiment found LLM counterarguments improved group flexibility and satisfaction while AI mediation boosted minority participation but lowered psychological safety.
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.
LLM facilitation in group charity allocation leaves consensus and participation equity unchanged while shifting specific allocations up to 5.5 points and increasing perceived trust.
Interviews with 16 qualitative researchers identify efficiency, ownership, and trust as key factors shaping preferences for AI as a supportive assistant rather than a full collaborator or supervisor in qualitative data analysis.
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
The SoccerNet 2026 Challenges benchmarked 427 teams across five soccer video understanding tasks, with leading submissions improving over baselines on all tasks.
citing papers explorer
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Rationalize: Shared Semantic Reasoning for Human-AI Alignment
Rationalize proposes a role-pair framework for shared semantic reasoning to support bidirectional human-AI alignment through explicit rationalization of intent.
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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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GroupEnvoy: A Conversational Agent Speaking for the Outgroup to Foster Intergroup Relations
An AI agent representing outgroup views in ingroup chat directionally reduced intergroup anxiety and improved perspective-taking versus passive document exposure.
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Investigating LLM-Powered Dissenting Minority Support in Power-Imbalanced Group Decision-Making: Counterargument and Mediation as Intervention Strategies
An experiment found LLM counterarguments improved group flexibility and satisfaction while AI mediation boosted minority participation but lowered psychological safety.
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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.
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Real-Time Group Dynamics with LLM Facilitation: Evidence from a Charity Allocation Task
LLM facilitation in group charity allocation leaves consensus and participation equity unchanged while shifting specific allocations up to 5.5 points and increasing perceived trust.
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Not a Collaborator or a Supervisor, but an Assistant: Striking the Balance Between Efficiency and Ownership in AI-incorporated Qualitative Data Analysis
Interviews with 16 qualitative researchers identify efficiency, ownership, and trust as key factors shaping preferences for AI as a supportive assistant rather than a full collaborator or supervisor in qualitative data analysis.
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Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
A survey of user studies on LLM use in programming that identifies interaction behaviors, mixed benefits and weaknesses, and factors influencing human and task performance.
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SoccerNet 2026 Challenges Results
The SoccerNet 2026 Challenges benchmarked 427 teams across five soccer video understanding tasks, with leading submissions improving over baselines on all tasks.