Tag: AI reliability
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LLM Deception on Benign Prompts: New Insights & Metrics
Explore how large language models self-initiate deception on benign prompts and learn about new metrics assessing AI trustworthiness.
DAPPr: Possibilistic Uncertainty for Reliable Deep Learning
Discover DAPPr, a novel framework improving epistemic uncertainty quantification in deep learning for more reliable predictions on unseen data.
Reliable Multimodal Circuit-to-Verilog Code Generation
Discover VeriGround, a breakthrough model enhancing accuracy and trust in multimodal circuit-to-Verilog code generation by tackling the Mirage effect.
Understanding and Mitigating Biases in Transformer AI
Explore key biases in transformer-based AI systems and learn strategies to ensure fair, reliable, and ethical AI deployment.
HalluHunter: Automated Detection of Factual Errors in LLMs
Discover HalluHunter, an iterative method that uncovers factual errors in large language models to improve accuracy and reliability.