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Balancing Performance and Reliability: How Model Merging Mitigates the Alignment Tax in LLMs

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MERA: A Principled Approach to Reducing Language Model Errors Through Adaptive Steering

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Calibrating Language Model Open-Endedness with Generation Space Size

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FOSSIL: A New AI Framework for Accurate Mpox Diagnosis in Data-Scarce Settings

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A New Measure for LLM Self-Awareness: The Refusal Index

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New Research Introduces COM-BOM: Optimizing AI Learning for Both Correctness and Confidence

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A New Approach to Quantifying Uncertainty in Large Language Models for Medical Diagnosis

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Unpacking LLM Struggles with Causality: The Role of Uncertainty and Overconfidence

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Unlocking Dynamic Problem-Solving in AI with Explanatory Verifiers

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Exploring Trust and Understanding in Advanced Language Models: A Berkman Klein Center Discussion

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Unlocking Agent-Based Models: How Automatic Differentiation Enhances Simulation and Calibration

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AI’s Blind Spots: Uncovering Bias and Overconfidence in Hate Speech Detection

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Keeping AI Models Honest: Calibrating Predictions in Unseen Scenarios

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Twin-Boot: Integrating Uncertainty into Deep Learning Optimization

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AI Models and Uncertainty: How Reinforcement Learning Can Lead to Overconfident Predictions

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Enhancing Machine Learning Reliability Through Uncertainty-Aware Prediction

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How Large Language Models Can Better Express Their Uncertainty to Humans

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Enhancing LLM Reliability: Learning from a Model’s Own Confidence

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Navigating Uncertainty: New Insights on Model Calibration in Image Classification Under Dataset Shifts

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