Google TechTalks

Google TechTalks

Differential PrivacyMachine LearningData PrivacyLarge Language Models (LLMs)Large Language ModelsDeep LearningAI SafetyPrompt EngineeringMachine Learning SecurityData poisoningMembership Inference AttacksCopyright InfringementNatural Language ProcessingData SecurityFine-tuningPrivacy AuditingLLM securityFederated LearningEthics of AIAdversarial AttacksStochastic Gradient DescentMatrix FactorizationPrivacy AlgorithmsLower BoundsModel EvaluationImage GenerationModel MemorizationMachine learning vulnerabilitiesSynthetic Data GenerationMachine Learning PrivacyRetrieval Augmented Generation (RAG)AI SecurityLanguage ModelsContinual CountingGenerative AIStreaming AlgorithmsApproximation AlgorithmsData MemorizationPrivacyPrivacy-Preserving Data AnalysisInformation Theory
Machine Text Detectors are Membership Inference Attacks
Machine Text DetectionMembership Inference AttacksLarge Language Models

Machine Text Detectors are Membership Inference Attacks

This research reveals that machine text detection and membership inference attacks, traditionally studied as separate problems, are fundamentally linked both theoretically and empirically, sharing optimal methods and exhibiting high cross-task transferability.

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Cascading Adversarial Bias from Injection to Distillation in Language Models
Large Language Models (LLMs)Retrieval Augmented Generation (RAG)Adversarial Attacks

Cascading Adversarial Bias from Injection to Distillation in Language Models

RAG systems, designed to enhance LLM accuracy and personalization, are vulnerable to 'Phantom' trigger attacks where a single poisoned document can manipulate outputs to deny service, express bias, exfiltrate data, or generate harmful content.

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Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?
LLM training dataBPE tokenizersData mixture inference

Data Mixture Inference: What do BPE Tokenizers Reveal about their Training Data?

BPE tokenizers, often overlooked, provide a transparent and accessible window into the secret data mixtures used to train large language models.

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