Tag: AI reliability
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Enhancing LLM Reliability with Scientific Decoding Constraints
Improve large language models' accuracy by 12% using scientific knowledge-driven decoding constraints for reliable, domain-specific AI outputs.
Reducing Hallucinations in Language Models with Composite Architecture
Explore a novel composite architecture combining instruction refusal and structural gating to reduce hallucinations in large language models effectively.
SELFDOUBT: Efficient Uncertainty Quantification for Reasoning LLMs
Discover SELFDOUBT, a novel method using the Hedge-to-Verify Ratio to quantify uncertainty in reasoning LLMs with high accuracy and low cost.
Diagnosing Surface Compliance in Large Language Models
Explore how surface compliance affects memory editing in large language models and why true internal modification is crucial for reliable AI.
Knowledge-Weighted Fine-Tuning to Improve AI Uncertainty
Enhance AI reliability with knowledge-weighted fine-tuning that teaches models when to say 'I don't know' and reduces hallucinations.