Analysis of 1,223 AI-HCI papers shows declining focus on human epistemic sovereignty and rising optimization of autonomous agents, leading to a proposal for scaffolded cognitive friction via multi-agent systems to preserve human cognitive agency.
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Fine-tuned RoBERTa achieves 0.62 macro-F1 on 900 Reddit comments, outperforming best zero-shot LLM at 0.50, with largest gap on detecting belief propagation.
A context-aware synthetic augmentation framework with a hybrid clinical-language model improves psychological defense mechanism classification to 58.26% accuracy and 24.62% macro-F1 in low-resource conditions, outperforming the DMRS Co-Pilot baseline.
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.
citing papers explorer
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Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction
Analysis of 1,223 AI-HCI papers shows declining focus on human epistemic sovereignty and rising optimization of autonomous agents, leading to a proposal for scaffolded cognitive friction via multi-agent systems to preserve human cognitive agency.
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Long Live Fine-Tuning: Task-Specific Transformers Outperform Zero-Shot LLMs for Misinformation Response Classification on Reddit
Fine-tuned RoBERTa achieves 0.62 macro-F1 on 900 Reddit comments, outperforming best zero-shot LLM at 0.50, with largest gap on detecting belief propagation.
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Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation
A context-aware synthetic augmentation framework with a hybrid clinical-language model improves psychological defense mechanism classification to 58.26% accuracy and 24.62% macro-F1 in low-resource conditions, outperforming the DMRS Co-Pilot baseline.
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A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring
Ultra-brief student concern texts analyzed with NLP associate with lower physical activity during academic concern weeks and poorer sleep plus lower heart rate variability during emotional exhaustion weeks, complementing wearable sensing.