LLM chat systems show large differences in reference quantity and quality, but users rarely click or engage with them.
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D2D adaptively prioritizes informative attribute queries and times recommendations in conversational search, yielding 22-30% higher target accuracy and shorter conversations than baselines in simulations.
Medium personality expression in LLM agents yields the most positive user perceptions in goal-oriented tasks, further improved by trait alignment.
Lived experience with misophonia shows trauma-informed design must address both sensory trauma from audiovisual platforms and epistemic harm from repeated dismissal of users' bodily accounts.
Omakase monitors project documents to infer timely queries and distills research reports into actionable suggestions that users rated significantly more useful than raw reports.
citing papers explorer
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Analyzing the Presentation, Content, and Utilization of References in LLM-powered Conversational AI Systems
LLM chat systems show large differences in reference quantity and quality, but users rarely click or engage with them.
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Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants
D2D adaptively prioritizes informative attribute queries and times recommendations in conversational search, yielding 22-30% higher target accuracy and shorter conversations than baselines in simulations.
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Vibe Check: Understanding the Effects of LLM-Based Conversational Agents' Personality and Alignment on User Perceptions in Goal-Oriented Tasks
Medium personality expression in LLM agents yields the most positive user perceptions in goal-oriented tasks, further improved by trait alignment.
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When Sounds Hurt and Voices Aren't Heard: An Experience Report on Misophonia, Sensory Trauma, and Trauma-Informed Design
Lived experience with misophonia shows trauma-informed design must address both sensory trauma from audiovisual platforms and epistemic harm from repeated dismissal of users' bodily accounts.
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Omakase: proactive assistance with actionable suggestions for evolving scientific research projects
Omakase monitors project documents to infer timely queries and distills research reports into actionable suggestions that users rated significantly more useful than raw reports.