Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
LLM chat systems show large differences in reference quantity and quality, but users rarely click or engage with them.
LLM-based SE tools lack stable ground truth and deterministic outputs, making standard evaluation assumptions invalid and requiring new approaches for reliable assessment.
citing papers explorer
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Effects of Generative AI Errors on User Reliance Across Task Difficulty
Higher generative AI error rates reduce user reliance, but task difficulty does not significantly moderate this effect.
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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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Evaluation of LLM-Based Software Engineering Tools: Practices, Challenges, and Future Directions
LLM-based SE tools lack stable ground truth and deterministic outputs, making standard evaluation assumptions invalid and requiring new approaches for reliable assessment.