LLM chat systems show large differences in reference quantity and quality, but users rarely click or engage with them.
Vera Liao, Larry Chan, I-Hsiang Lee, Michael Muller, and Mark O
4 Pith papers cite this work, alongside 56 external citations. Polarity classification is still indexing.
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Explanation preferences for AI privacy redaction vary systematically with domain and redaction amount; giving users their preferred styles raises trust over random or no explanations.
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.
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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Exploring the Interaction of Explanation Styles, Context, and Trust of AI Privacy Redaction in AI-mediated Interactions
Explanation preferences for AI privacy redaction vary systematically with domain and redaction amount; giving users their preferred styles raises trust over random or no explanations.
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From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.
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Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems
Introduces L2-Bench benchmark for AI feedback in language education across six dimensions and identifies explainability pitfalls in AI-generated explanations that appear helpful but are flawed.