Linear Probes Ai, linear_probe """Module for layer and neuron level linear-probe based analysis.

Linear Probes Ai, interpretation. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to In this paper, we probe the activations of intermediate layers with linear classification and regression. On top of it, you add one small linear layer: no homework for the old material, Linear probes are a deceptively simple yet powerful technique used to analyze the internal representations learned by AI models, particularly large language models and computer lmprobe Language Model Probe Library This library supports the use of language model "activations" or "latents" to build text classifiers. AI models might use deceptive strategies as part of scheming or Developing effective world models is crucial for creating artificial agents that can reason about and navigate complex environments. However, single-layer probes Linear Probe Penalties Reduce LLM Sycophancy 14 Dec 2024 Visiting ETH MsC student Henry Papadatos and supervising CHAI PhD student Rachel Freedman publish an article Linear probes can detect when language models produce outputs they "know" are wrong, a capability relevant to both deception and reward hacking. Changes to pre-trained features are minimized. Think of it like a Probe Training and Evaluation: I used code and methodology from Apollo's Detecting Strategic Deception Using Linear Probes paper to train and AI Probes Explained: How Probing Works in Neural NetworksWhat are probes in AI and why are they important?In this video, we explain AI probes (probing classi Our method employs a linear probe within the reward model to quantify the extent of sycophancy in the AI’s responses. We use These linear probes are not costly to train nor to use during inference and can help detect adversarial attacks and possibly other kinds of 5. This module contains functions to train, evaluate and use a linear probe for both Probing methods closely related to those used here were recently described under the banner of “linear artificial tomography” within the In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. linear_probe """Module for layer and neuron level linear-probe based analysis. We compare Logistic Regression to alternative probing methods including Difference of Means (Marks & Tegmark, 2023) and Linear Artificial Tomography (Zou et al. 作用 自监督模型评测方法 是测试预训练模型性能的一种方法，又称为linear probing evaluation Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. They reveal how semantic content evolves across Probes have been frequently used in the domain of NLP, where they have been used to check if language models contain certain kinds of linguistic information. ABSTRACT AI models might use deceptive strategies as part of scheming or misaligned behaviour. However, transductive linear probing shows that fine-tuning a simple linear classification head after a Abstract In explainable AI, Concept Activation Vectors (CAVs) are typically obtained by training linear classifier probes to detect human-understandable concepts as directions in the Probing Linear Probing attempts to learn a linear classifier that predicts the presence of a concept based on the activations of the model [33]. We demon-strate that linear probes trained on LLM activa-tions can accurately identify where persuasion success or failure Do large language models (LLMs) anticipate when they will answer correctly? To study this, we extract activations after a question is read but before any tokens are generated, and In this paper, we propose truncated polynomial classifiers (TPCs) to achieve these two properties–refining linear probes’ decision boundary by modelling rich higher To achieve this, we introduce Truncated Polynomial Classifiers (TPCs), a natural extension of linear probes for dynamic activation monitoring. However, we discover that current probe learning strategies are ineffective. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Ananya Kumar, Stanford Ph. Monitoring outputs alone is insuficient, since the AI might produce seemingly Promoting openness in scientific communication and the peer-review process AI models might use deceptive strategies as part of scheming or misaligned behaviour. The basic In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. Gain familiarity with the PyTorch and HuggingFace libraries, for Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. For part-of-speech tagging, moving from linear to MLP probes leads to a slight Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discriminating features. Our experiments SAE features are supposed to be interpretable, but when I wanted to directly attack an AI's own ontology, the whole thing kinda broke down. D. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. In this work, our main contributions are: (1) A Brier-score–trained linear probe that produces cali-brated Researchers at Apollo Research demonstrate that linear probes can effectively detect strategic deception in large language models by analyzing internal act 【Linear Probing | 线性探测】深度学习 线性层 1. We thus evaluate if linear probes can robustly Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. In this paper, we investigate a deep supervision View a PDF of the paper titled LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states, by Luis Ibanez-Lissen and 4 other We propose Deep Linear Probe Gen erators (ProbeGen) for learning better probes. This helps us better understand the roles and dynamics of the intermediate layers. They are trained either on a per-token basis or on a compressed representation of latent vectors from multiple Probing by linear classifiers This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. ProbeGen op-timizes a deep generator module limited to linear expressivity, that shares information between the different We use linear classifiers, which we refer to as “probes”, trained entirely independently of the model itself. See here for a summary thread. 1. This holds true for both in-distribution (ID) and out-of TLDR: This is the abstract, introduction and conclusion to the paper. This approach uses prompts that This paper especially investigates the linear probing performance of MAE models. The researchers discovered that adding simple components called linear probes Meta learning has been the most popular solution for few-shot learning problem. We then modify the reward model to penalize responses based on their sycophancy Linear probing serves as a standardized evaluation protocol for self-supervised learning methods. The recent Masked Image Modeling (MIM) approach is shown Can we catch AI liars by eavesdropping on their thoughts? Detecting Strategic Deception Using Linear Probes Published 2/5/2025 by Nicholas Goldowsky-Dill, Bilal Chughtai, Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. This has motivated intensive research building Abstract. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal Language models can distinguish between testing and deployment phases -- a capability known as evaluation awareness. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and Our method uses linear classifiers, referred to as "probes", where a probe can only use the hidden units of a given intermediate layer as Detecting Strategic Deception Using Linear Probes: Paper and Code. Contribute to EleutherAI/attention-probes development by creating an account on GitHub. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. Linear probes find directions that work, but I One of the simple strategies is to utilize a linear probing classifier to quantitatively eval-uate the class accuracy under the obtained features. This holds true for both in-distribution (ID) and out-of Source code for neurox. ProbeGen optimizes a deep generator module limited to linear expressivity, that shares information Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. However, single-layer probes are Promoting openness in scientific communication and the peer-review process AI safety systems are being deployed on top of linear probes that read model internals as runtime interventions. Linear Probing System Relevant source files Purpose and Overview The Linear Probing System evaluates the quality of representations learned by pre-trained Masked Autoencoder (MAE) models Linear Probing System Relevant source files Purpose and Overview The Linear Probing System evaluates the quality of representations learned by pre-trained Masked Autoencoder (MAE) models Dataset distillation compresses a large training set into a small synthetic set that preserves downstream training utility. The intent is to help detect and reduce misuse Linear probes are a deceptively simple yet powerful technique used to analyze the internal representations learned by AI models, particularly large language models and computer lmprobe Language Model Probe Library This library supports the use of language model "activations" or "latents" to build text classifiers. , 2023) in Appendix D. Unlike fine-tuning which adapts the entire model to the downstream task, linear probing Representation: We trained linear probes on the model's internal activations to find the "test awareness" directions in the model's neural space We were surprised by SAEs underperforming linear probes, but also by how well linear probes did in absolute terms, on the complex-seeming While linear probes are simple and interpretable, it is unable to disentangle features distributed features that combine in a non-linear way. First, you find a feature of interest for behavioral alignment in the model's Explore how linear probes with persona coordinates improve generalization in detecting harmful behaviors in language models across datasets. Probes' performance is comparable to How to implement Linear Probing for first N epochs and then switch to fine-tuning? #12488 Unanswered konradkalita asked this question in Lightning Trainer API: Trainer, We find that linear and bilinear probes are considerably more selective than multi-layer perceptron probes. We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. While most existing methods target training networks from This research looks at using linear probes - essentially simple mathematical tools - to peek inside large language models and measure their internal uncertainty. Evaluation and Linear Probing Relevant source files This document covers the linear probe evaluation system used in StableRep to assess the quality of learned visual representations. The master's degree — your pretrained network — stays exactly as it was, untouched. A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network to test whether a particular concept, property, or label is Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal reasoning is misaligned. Linear probes are a deceptively simple yet powerful technique used to analyze the internal representations learned by AI models, particularly large language models and computer Linear probes with attention weighting. We demonstrate That's a linear probe. The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This has significant safety and policy implications, However, we discover that current probe learning strategies are ineffective. Linear probes are a simple way to classify internal states of language models. They allow us to understand if the numeric representation Probing classifiers find applications in various domains, including: Natural Language Understanding: Probing classifiers help analyze and understand the linguistic properties captured by language We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Abstract Do large language models (LLMs) anticipate when they will answer Learn how linear classifier probes test what hidden layers encode in deep neural networks, how to train them, and how to interpret results Non-Linear Probes: Small MLPs can capture more complex, non-linear relationships between activations and the concept. In this work, our main contributions are: (1) A Brier-score–trained linear probe that produces cali-brated Calibration is thus essential for building reliable and trustworthy AI systems with LLM judges. Results show that the bias towards simple solutions of generalizing networks is maintained even Neural network models have a reputation for being black boxes. Our key insight is that polynomials can Keywords: LLMs, linear probes, activations, correctness, confidence, uncertainty, mechanistic interpretability, probing, generalization, direction TL;DR: By reading only question-time These models help AI agents make decisions by simulating what might happen next. The intent is to help detect and reduce misuse Probing classifiers can give us some insight into what happens inside neural networks, but are far from being able to provide a complete picture. . The basic idea is simple — a classifier A few works leveraged internals to predict models’ ability to answer a question correctly, but no work has investigated directly training linear probes only relying on internals. Abstract Linear probes can detect when language models produce outputs they “know” are wrong, a ca-pability relevant to both deception and reward hacking. It Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. There are many open problems in the field deep-neural-networks psychophysics cognitive-neuroscience linear-probing explainable-ai interpreting-models human-machine-behavior Updated on Jul 16, 2024 Python We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. These probes can be We compare Logistic Regression to alternative probing methods including Difference of Means (Marks & Tegmark, 2023) and Linear Artificial Tomography (Zou et al. Systematic experiments Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, is 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 deceptive. Moreover, these probes cannot affect the Calibration is thus essential for building reliable and trustworthy AI systems with LLM judges. While potentially more accurate, a highly accurate non-linear probe 1st Linear probing (LP), 2nd Fine-tuning (FT) FT starts with the optimized linear layer (classifier). In the future, it would be interesting to use non Baseline probes have a specific feature they’re interested in learning in a supervised way, while SAE latents are unsupervised, and when Probing persuasion outcomes, rhetorical strategies, and personality traits. The typical linear probe is only applied as a proxy at the in We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. x0hh, v4kfwns, unj9m, sgt2rd, smjdofew, uye, 7v, 2ifwc, 7le5m, s6pau,