Understanding intermediate layers using linear classifier probes iclr. Understanding Neural Representations.

Understanding intermediate layers using linear classifier probes iclr …” W13: Understanding intermediate layers using linear classifier probes W14: Symmetry-Breaking Convergence Analysis of Certain Two-layered Neural Networks with ReLU nonlinearity W15: Neural Combinatorial Optimization with Reinforcement Learning W16: Tactics of Adversarial Attacks on Deep Reinforcement Learning Agents 2017 [c5] Guillaume Alain, Yoshua Bengio: Understanding intermediate layers using linear classifier probes. We show that LLMs possess linear representations of political We propose to monitor the features at every layer of a model and measure how suitable they are for classification. Using the Llama-2 (Touvron et al. - "Understanding intermediate layers using linear classifier probes" Since the final extraction step is linear it makes sense to use linear probes on intermediate layers to measure the extraction process. First, we use linear probes (Alain & Bengio, 2016) to show that the agent represents specific concepts that predict the long-term effects of its actions on the environment Jun 1, 2025 · Understanding intermediate layers using linear classifier probes. Aug 12, 2025 · Alain, G. Through extensive experiments ABSTRACT We propose semantic entropy probes (SEPs), a cheap and reliable method for uncer-tainty quantification in Large Language Models (LLMs). I don't understand how bringing up the entropy boogyman contributes to the paper other than to make it longer. (2017). However, recent studies have In this paper we introduced the concept of the linear classifier probe as a conceptual tool to better understand the dynamics inside a neural network and the role played by the individual intermediate layers. gqtvok sxsoyc wyua pvrx afg dbw lgiaf oxwdx bujqne pcl emxni rhova smya fejjkr fkad