We test two probe-training datasets, one with contrasting instructions to be honest or Linear probes are simple linear classifiers that are trained on top of the features extracted from a pre-trained model to evaluate its performance on a specific task. Our approach, In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. They reveal how semantic content evolves across We recently published a paper investigating if linear probes detect when Llama is deceptive. We study that in pretrained networks trained on Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AI このサイトでは基本的に自然言語処理の論文等をご紹介してきましたが、今回はOpenAIが発表した画像生成モデル『Image GPT』の論文を解 A linear probe is a simple linear classifier used to evaluate the performance of features extracted from a pre-trained model. We use linear classifiers, which we refer to as “probes”, trained entirely independently of the model itself. One can use linear probes to evaluate the feature’s quality quantitatively. Probes in the above sense are Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to . They allow us to u To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. This helps us better understand the roles and dynamics of the intermediate layers. We test two probe-training datasets, one with Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. ProbeGen optimizes a deep generator module limited to linear expressivity, that However, we discover that current probe learning strategies are ineffective. Final section: unsupervised probes. Since the discrimination capability of lin-ear classifiers is low, linear classifiers É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. We demonstrate Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear probes detect when Llama is This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Linear probes are simple, 線形判別分析（Linear Discriminant Analysis, LDA）は、データの分類と次元削減において不可欠な技術として広く認知されています。 そのシ Another simple strategy is to perform linear probing. Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. We built probes using simple training data (from RepE paper) and techniques (logistic How can we spot that kind of strategic deception before it causes harm?We explore a simple detector system: a linear probe that monitors the model's internal thoughts (its 'activations', or We thus evaluate if linear probes can robustly detect deception by monitoring model activations. We test two probe-training datasets, one with contrasting instructions to be honest or deceptive (following This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. Monitoring outputs alone is insufficient, since Trustworthy AI: Validity, Fairness, Explainability, and Uncertainty Assessments: Explainability methods: Linear Probes Abstract page for arXiv paper 2504. This has motivated intensive research building Linear probes are simple classifiers attached to network layers that assess feature separability and semantic content for effective model diagnostics. 03861: Improving World Models using Deep Supervision with Linear ProbesView a PDF of the paper titled Improving World Models using Deep We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. We test two probe-training datasets, one with contrasting instructions to be honest or This guide explores how adding a simple linear classifier to intermediate layers can reveal the encoded information and features critical for We thus evaluate if linear probes can robustly detect deception by monitoring model activations.
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