Tag: transformer models
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Patch-Effect Graph Kernels for Transformer Interpretability
Discover how patch-effect graph kernels enhance interpretability in large language models by analyzing transformer computations with graph learning.
CircuitFormer: AI Model for Analog Circuit Design Automation
Discover CircuitFormer, an AI language model improving analog circuit design with 83% success and efficient tokenization for scalable EDA automation.
HyperLens: Measuring Cognitive Effort in Large Language Models
Discover HyperLens, a tool that quantifies cognitive effort in LLMs by tracking fine-grained confidence trajectories during inference.
Transformer Memory Geometry: Resolving Conflicts & Hallucinations
Explore how transformer models handle memory conflicts and hallucinations using attractor geometry for improved AI reliability and accuracy.
Optimizing Attention in Large Vision-Language Models
Discover how inefficiencies in attention mechanisms affect large vision-language models and explore new strategies for improved AI performance.