PrivacyPAD: A Reinforcement Learning Framework for Dynamic Privacy-Aware Delegation

PrivacyPAD trains a routing agent to decide which parts of a user’s prompt stay private and which are shared. It strikes a careful balance between data protection and performance, allowing users to safely benefit from powerful external models.

See the full article here on arXiv.

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Beyond the final layer: Intermediate representations for better multilingual calibration in large language models

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Navigating the Alignment-Calibration Trade-off: A Pareto-SuperiorFrontier via Model Merging