Tag: AI reliability
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How LLMs Can Explicitly Express Uncertainty
Discover how large language models (LLMs) improve accuracy by explicitly expressing uncertainty with global confidence and local signaling methods.
Detecting Untrustworthy Boundaries in Black-box LLMs
Discover how GMRL-BD detects bias and untrustworthy boundaries in black-box LLMs using bias-diffusion and multi-agent reinforcement learning.
LatentAudit: Real-Time Faithfulness Monitoring for RAG AI
LatentAudit enables real-time, verifiable faithfulness monitoring for retrieval-augmented generation AI, boosting reliability and reducing hallucinations.
Automated Evolutionary Search for Uncertainty Quantification
Discover how evolutionary search enhances automated uncertainty quantification methods, boosting performance and generalization in AI models.
Limits of AI Safety Verification Using Kolmogorov Complexity
Explore the intrinsic limits of AI safety verification via Kolmogorov complexity and the need for novel methods to ensure reliable AI policy compliance.