Introduces DelegateCI-Bench (3167 samples) and a CI-guided RL query rewriter that improves privacy-utility tradeoff by up to +10.1 utility over on-device baselines.
PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles
4 Pith papers cite this work. Polarity classification is still indexing.
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verdicts
UNVERDICTED 4representative citing papers
AIR excels on label-remapping classification tasks while KNN retrieval leads on closed-book QA and fine-tuning leads on structured extraction and event-order reasoning, showing task-dependent adaptation performance.
ASTRA is an automated closed-loop framework that discovers, retrieves, and evolves jailbreak attack strategies for LLMs using a dynamic three-tier strategy library and outperforms baselines in black-box settings.
The work proposes and evaluates techniques to reduce PII exposure from image context in online vision-language models while preserving utility for downstream applications.
citing papers explorer
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Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation
Introduces DelegateCI-Bench (3167 samples) and a CI-guided RL query rewriter that improves privacy-utility tradeoff by up to +10.1 utility over on-device baselines.
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Automated Instruction Revision (AIR): A Structured Comparison of Task Adaptation Strategies for LLM
AIR excels on label-remapping classification tasks while KNN retrieval leads on closed-book QA and fine-tuning leads on structured extraction and event-order reasoning, showing task-dependent adaptation performance.
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ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs
ASTRA is an automated closed-loop framework that discovers, retrieves, and evolves jailbreak attack strategies for LLMs using a dynamic three-tier strategy library and outperforms baselines in black-box settings.
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Assessing Privacy Preservation and Utility in Online Vision-Language Models
The work proposes and evaluates techniques to reduce PII exposure from image context in online vision-language models while preserving utility for downstream applications.