Tag: mechanistic interpretability
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Improving Confidence Calibration in Large Language Models
Discover how new research closes the gap between confidence scores and accuracy in large language models using advanced interpretability techniques.
Sparse Auto-Encoders and Holistic Meaning in LLMs
Explore how sparse auto-encoders challenge and enrich holistic views on meaning in large language models (LLMs) through latent feature analysis.
NIST Seeks Input on AI Executive Order Compliance
NIST requests stakeholder feedback to ensure responsible, safe, and ethical AI development under the Executive Order on AI governance.
Understanding Neural Networks with Sparse Circuits
Explore how sparse circuits simplify neural networks, enhancing transparency, safety, and efficiency in AI systems for better interpretability.