Tag: AI language models
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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.
Limits of Sequence Learning in Early Word Acquisition
Explore how distributional sequence learning models struggle with generalizing word meanings in early language development.
Enhancing Chess AI Reasoning with Fine-Tuning & RL
Discover how fine-tuning and reinforcement learning improve AI reasoning and move quality in chess, surpassing leading models with faithful decision-making...
LLM Sensitivity to Patient Question Framing in Medical QA
Explore how patient question phrasing affects large language models' responses in medical QA and the implications for reliable healthcare advice.
Cactus: Fast Auto-Regressive Decoding with Speculative Sampling
Discover Cactus, a method that speeds up auto-regressive decoding using constrained acceptance speculative sampling while maintaining output quality.