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R. Wang

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Privacy Audits for Clinical Large Language Models

Verified Extraction is introduced, an auditing framework that distinguishes identifiers attributable to fine-tuning data from spurious or prior-driven outputs and quantifies recoverable leakage under explicit query budgets.

F. Pollet, Tong Wang, Rahul Gupta et al. · 0 citations
Aug 2026

REFINE: Closing the Loop Between Large Language Models and Symbolic Rules in Clinical NLP

This study investigates whether large language models (LLMs) can assist in identifying extraction errors and generating candidate rules to improve symbolic clinical NLP systems and suggests that LLMs can support scalable rule refinement for symbolic clinical NLP systems.

N. Wang, A. Kakadiaris, C. Li et al. · 0 citations