Tag: AI interpretability
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Non-Surjective Nature of Steered LLM Activations
Discover why steered LLM activations are non-surjective, limiting prompt-based control and impacting AI interpretability and safety research.
ConceptTracer: Interactive Tool for Neural Concept Analysis
Explore neural network interpretability with ConceptTracer, analyzing concept saliency and selectivity to enhance AI transparency and understanding.
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.
MetaSAEs: Enhancing Atomic Sparse Autoencoder Latents
Discover how MetaSAEs use joint training with decomposability penalties to create more atomic, interpretable sparse autoencoder latents for safer AI models...
Controlling LLM Sycophancy by Verbalizing Assumptions
Explore how verbalizing assumptions in LLMs helps explain and control sycophantic behavior, improving AI safety and response objectivity.