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Weight Oracles: Reading Neural Network Weights with Language Models
Krishna Kabra, Constantin Venhoff, Christian Schroeder de Witt
Interpretability methods for neural networks are predominantly reactive: they analyse activations produced during specific forward passes, requiring known inputs to find hidden cap…
Too Late to Recall: Explaining the Two-Hop Problem in Multimodal Knowledge Retrieval
Constantin Venhoff, Ashkan Khakzar, Sonia Joseph +2
Training vision language models (VLMs) aims to align visual representations from a vision encoder with the textual representations of a pretrained large language model (LLM). Howev…
Reasoning-Finetuning Repurposes Latent Representations in Base Models
Jake Ward, Chuqiao Lin, Constantin Venhoff +1
Backtracking, an emergent behavior elicited by reasoning fine-tuning, has been shown to be a key mechanism in reasoning models' enhanced capabilities. Prior work has succeeded in m…
Understanding Reasoning in Thinking Language Models via Steering Vectors
Constantin Venhoff, Iván Arcuschin, Philip Torr +2
Recent advances in large language models (LLMs) have led to the development of thinking language models that generate extensive internal reasoning chains before producing responses…
Mixture of Experts Made Intrinsically Interpretable
Xingyi Yang, Constantin Venhoff, Ashkan Khakzar +4
Neurons in large language models often exhibit \emph{polysemanticity}, simultaneously encoding multiple unrelated concepts and obscuring interpretability. Instead of relying on pos…
SAGE: Scalable Ground Truth Evaluations for Large Sparse Autoencoders
Constantin Venhoff, Anisoara Calinescu, Philip Torr +1
A key challenge in interpretability is to decompose model activations into meaningful features. Sparse autoencoders (SAEs) have emerged as a promising tool for this task. However,…