DPUA is a two-phase framework that aligns LLM uncertainty expressions with human disagreement distributions in subjectivity analysis while preserving task performance.
A survey on uncertainty quantification of large language models: Taxonomy, open research challenges, and future directions,
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SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
Chatbot AI systems often fail complex needs while projecting authority, contributing to deskilling, labor displacement, economic concentration, and high environmental costs, so alternative pluralistic and task-specific designs are needed.
citing papers explorer
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Aligning LLM Uncertainty with Human Disagreement in Subjectivity Analysis
DPUA is a two-phase framework that aligns LLM uncertainty expressions with human disagreement distributions in subjectivity analysis while preserving task performance.
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SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
SWAY quantifies sycophancy in LLMs via shifts under linguistic pressure and a counterfactual chain-of-thought mitigation reduces it to near zero while preserving responsiveness to genuine evidence.
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What if AI systems weren't chatbots?
Chatbot AI systems often fail complex needs while projecting authority, contributing to deskilling, labor displacement, economic concentration, and high environmental costs, so alternative pluralistic and task-specific designs are needed.