RLAA is a localized adversarial anonymization framework that adds an arbitrator to filter ghost leaks and enforce rational early stopping, yielding superior privacy-utility trade-offs on benchmarks compared to greedy baselines.
Rakshith Shetty, Bernt Schiele, and Mario Fritz
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The work proposes and evaluates techniques to reduce PII exposure from image context in online vision-language models while preserving utility for downstream applications.
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
citing papers explorer
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Look Twice before You Leap: A Rational Framework for Localized Adversarial Anonymization
RLAA is a localized adversarial anonymization framework that adds an arbitrator to filter ghost leaks and enforce rational early stopping, yielding superior privacy-utility trade-offs on benchmarks compared to greedy baselines.
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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.
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LLM Harms: A Taxonomy and Discussion
Proposes a five-bucket taxonomy of LLM harms and calls for dynamic auditing, but the systematic review behind it is not reproducible and contains mismatched citations.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.