Linear probes ai. Final section: unsupervised probes.

Linear probes ai The best-performing CLIP model, using ViT-L/14 archiecture and 336-by-336 pixel images, achieved the state of the art in 21 of the 27 datasets, i. They are trained either on a per-token basis or on a compressed representation of latent vectors from multiple tokens. Oct 5, 2016 · Neural network models have a reputation for being black boxes. ai community, who are engaged in developing and deploying advanced machine learning models, may find probing classifiers valuable for the following reasons: We use linear classifiers, which we refer to as “probes”, trained entirely independently of the model itself. Nov 29, 2024 · Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Apr 4, 2025 · Since impostors sometimes don't perform "lying" actions, that label likely provides higher signal for deception than lying. the linear probe) is trained on an interpretability task in the activation space of layer l (hence Ml i). Increase of the probe's accuracy on non-related features w. Technically, it analyzes the model's internal representations to detect when it's being overly agreeable rather than truthful. By providing new ways to visualize and analyze weight space learning, this technique could lead to more interpretable and efficient AI systems. The Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. We then modify the reward model to penalize responses based on their sycophancy score. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. ProbeGen optimizes a deep generator module limited to linear expressivity, that shares information between the different probes. Your hard work is a beacon of hope! Vascular imaging Thyroid and breast examinations Musculoskeletal imaging Small parts scanning (e. For instance, a probe trained only on mathematical comparisons like "9 is greater than 7" (true) transferred well to identifying the truth of Spanish-English translations like "Gato means We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. The process works in three main steps: 1) The probe learns to recognize patterns in the AI's internal states that correlate with Linear-Probe Classification: A Deep Dive into FILIP and SODA | SERP AIhome / posts / linear probe classification A source of valuable insights, but we need to proceed with caution: É A very powerful probe might lead you to see things that aren’t in the target model (but rather in your probe). Detecting Strategic Deception Using Linear Probes does a more thorough job of investigating white-box methods for control empirically. The study evaluates linear probes for detecting AI deception, achieving high accuracy in distinguishing honest from deceptive outputs, but concludes that cur Linear probes with attention weighting. Truthfulness probes are tools that assess and enhance the factual accuracy of AI outputs by analyzing hidden model activations and outputs. They employ statistical, geometric, and mechanistic methods—such as linear classifiers and calibration techniques—to distinguish true from false information. 3-70B responds deceptively. We demonstrate how this can be used to develop a better intuition about models and to diagnose potential problems. DNN trained on im-age classification), an interpreter model Mi (e. . Sep 19, 2024 · Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. This work could be crucial in developing more responsible and trustworthy AI systems, especially as language models become increasingly sophisticated and integrated into various applications. the input sample but related to the target sample. While simple, we demonstrate it greatly enhances probing methods, and also outperforms other Jul 2, 2025 · We show that linear probes can separate real-world evaluation and deployment prompts, suggesting that current models internally represent this distinction. Finally, good probing performance would hint at the presence of the said property, which has the potential of being used in making final decisions to choose a label in the farthest layer of the neural network. " Mar 14, 2025 · 1. AI models might use deceptive strategies as part of scheming or misaligned behaviour. t. Dec 16, 2024 · Explainability methods: Linear Probes Last updated on 2024-12-16 | Edit this page Feb 6, 2025 · ABSTRACT AI models might use deceptive strategies as part of scheming or misaligned behaviour. This module contains functions to train, evaluate and use a linear probe for both layer-wise and neuron-wise analysis. Our method employs a linear probe within the reward model to quantify the extent of sycophancy in the AI’s responses. Forcing linear probes on top of LLM hidden layer activations to have a H2O. While they study a number of linear and nonlinear multilayered perceptrons, one could extend this idea to other classes of probes. The use of linear classifier probes offers a novel approach to unraveling the inner workings of neural network models. The approach proves particularly valuable for multi-turn settings and large-scale dataset analysis where prompting methods become computationally prohibitive. 4. Mar 29, 2023 · The fact that the original paper needed non-linear probes, yet could causally intervene via the probes, seemed to suggest a genuinely non-linear representation, and this could have gone either way. It then observes the responses from all probes, and trains an MLP classifier on them. "What is one grain of sand in the desert? analyzing individual neurons in deep nlp models. Oct 6, 2025 · This research looks at using linear probes - essentially simple mathematical tools - to peek inside large language models and measure their internal uncertainty. We demonstrate how this Apr 5, 2023 · Ananya Kumar, Stanford Ph. One of the simple strategies is to utilize a linear probing classifier to quantitatively eval-uate the class accuracy under the obtained features. Recent research shows these probes can diagnose, intervene, and improve model honesty, though Sep 30, 2025 · By moving beyond linear probes, the approach offers a more nuanced and adaptive method for detecting potential risks. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal reasoning is misaligned. Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. Learn about the construction, utilization, and insights gained from linear probes, alongside their limitations and challenges. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. Jun 17, 2024 · The probes seem to detect the concepts better in later layers. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. The beam has a convex shape that makes the transducer ideal for deeper organ imaging examinations. This describes some of the ways that most of our control work is conservative, and explains why we usually choose to do the research that way. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. Many studies have been conducted to assess the qual-ity of feature representations. We find that optimizing against this augmented reward model successfully reduces sycophantic behavior in multiple large open-source LLMs. Results linear probe scores are provided in Table 3 and plotted in Figure 10. AUROC for linear probes across datasets (y: train, and x: eval datasets). Think of it like a diagnostic tool that helps understand how sure a model is about its outputs. seealso:: `Dalvi, Fahim, et al. Dec 16, 2024 · Explainability methods: Linear Probes Last updated on 2024-12-16 | Edit this page Estimated time: 0 minutes Oct 14, 2024 · However, we discover that current probe learning strategies are ineffective. However, despite the widespread use of large The above safety case fits a linear probe, but this is likely too generous; the classifier could likely achieve high accuracy if there were any systematic difference between the testing and deployment examples, even if it’s a spurious correlation in the dataset that wouldn’t actually be exploitable. We test two probe-training datasets, one with contrasting instructions to Aug 1, 2025 · Linear probes are a simple way to classify internal states of language models. Given a model M trained on the main task (e. Monitoring outputs alone is insuficient, since the AI might produce seemingly benign outputs while their internal reasoning is misaligned. Oct 24, 2025 · By 2025, ultrasonic linear probes will become even more integrated with AI and machine learning, enhancing diagnostic accuracy. Setup Model: ViT (CLIP) Apr 4, 2022 · Pimentel et al. The typical linear probe is only Apr 4, 2025 · Developing effective world models is a crucial aspect of artificial intelligence, as it can enable agents to make accurate predictions of how the world will unfold, as well as how their actions will influence the world, and plan their actions accordingly (Ha & Schmidhuber, 2018). ai + Probing Classifiers The H2O. Oct 5, 2016 · 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. In this paper, we investigate a deep supervision technique for encouraging the development of a world model in a network trained end-to-end to predict the next observation. , testicular ultrasound) 2. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier Mar 19, 2025 · More About The goal is to train a linear probe on a biased dataset where gender is perfectly correlated with profession and de-bias the probe so that it generalizes to a test set where gender is not correlated with profession. Apr 23, 2024 · Related work Linear probes were originally introduced in the context of image models but have since been widely applied to language models, including in explicitly safety-relevant applications such as measurement tampering. linear probes [2], as clues for the interpretation. While deep supervision has been widely applied for task-specific learning, our focus is on Overview World models simulate environments to help AI plan actions Linear probes can be added to world models for better learning These probes extract specific information during training The approach improves performance across various environments It requires minimal computational overhead (10-15%) Plain English Explanation World models are AI systems that try to understand and predict Nov 1, 2024 · Baseline probes have a specific feature they’re interested in learning in a supervised way, while SAE latents are unsupervised, and when SAE probing we find the set of latents that is most closely aligned with the feature of interest. We built probes using simple training data (from RepE paper) and techniques (logistic regression). World models allow agents to reason about the environment and its dynamics. D. We Philips Lumify handheld ultrasound devices let you take high-quality images wherever and whenever you need them. They reveal how semantic content evolves across network depths, providing actionable insights for model interpretability and performance assessment. Oct 12, 2023 · Second, the researchers systematically tested whether linear "probes" trained on one dataset could accurately classify the truth of totally distinct datasets. Apr 25, 2024 · Using linear probes to dissect internal LLM embeddings to check for a hint of an internal world model. Oct 23, 2025 · Deep Linear Probe Generators represent a promising approach to understanding machine learning models' internal representations. Contribute to EleutherAI/attention-probes development by creating an account on GitHub. In that sense, baseline probes have more capacity for learning the specific feature of interest. e. In the context of AI, a ”world model Mar 28, 2023 · Omg idea! Maybe linear probes suck because it's turn based - internal repns don't actually care about white or black, but training the probe across game move breaks things in a way that needs smth non-linear to patch At this point my instincts said to go and validate the hypothesis properly, look at a bunch more neurons, etc. In addition to the generalizing networks trained on correct data, two types of intentionally flawed models are used for This post describes the simplest white-box method for AI control. Abstract Understanding network generalization and feature dis-crimination is an open research problem in visual recogni-tion. Jul 15, 2023 · Convex probes (also called curved linear probes) have a curved array that allows for a wider field of view at a lower frequency. r. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Final section: unsupervised probes. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Jun 2, 2025 · 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. We test these probes in more complicated and realistic environments where Llama-3. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing approaches. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal reasoning is misaligned. We also find that current safety evaluations are correctly classified by the probes, suggesting that they already appear artificial or inauthentic to models. Probes in the above sense are supervised This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. . g. To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining This is a work-in-progress repository for finding adversarial strings of tokens to influence Large Language Models (LLMs) in a variety of ways, as part of investigating generalization and robustness of LLM activation probes. """Module for layer and neuron level linear-probe based analysis. (2020a) argue that probing work should report the possible trade-offs between accuracy and complexity, along a range of probes g, and call for using probes that are both simple and accurate. We found that linear probe performance generalizes across datasets, though there remains some performance gap compared to on-distribution probes. By isolating layer-specific diagnostics, linear probes inform strategies for pruning, compression, and Oct 25, 2024 · This guide explores how adding a simple linear classifier to intermediate layers can reveal the encoded information and features critical for various tasks. included in the Cloppe Apr 2, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. Wireless connectivity and cloud-based data sharing will facilitate A. The authors trained a linear probe on layer 4 of pythia-70m-deduped and mean-pool over all token positions. Analysing Adversarial Attacks with Linear Probing Goal See what kind of features (if any) adversarial attacks find. Linear Probe Linear probes produce high-frequency sound waves, making them ideal for imaging superficial structures. We compare to a suite of baseline methods across 4 difficult probing regimes: 1) data scarcity, 2) class imbalance, 3) label noise, and 4) co-variate shift. EchoNous is pioneering a new AI POCUS era with Kosmos—a fusion of cart-based quality, handheld affordability, and AI-enhanced efficiency. Feb 5, 2025 · Detecting Strategic Deception Using Linear Probes: Paper and Code. Thus, we curate 113 linear probing datasets from a variety of settings and train linear probes on corresponding SAE latent activations (see Figure 2). The researchers are essentially trying to develop a confidence meter for AI. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing May 14, 2025 · What are probing classifiers and can they help us understand what’s happening inside AI models? - Blog post by Sarah Hastings-Woodhouse Aug 7, 2025 · The successful application of linear probes to persuasion analysis opens promising avenues for studying other complex AI behaviors. É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. We test two probe-training datasets, one with contrasting Dec 1, 2024 · The linear probe functions as a diagnostic tool that identifies specific neural patterns associated with sycophantic behavior in LLMs. By analyzing the outputs of these probes, researchers can gain deeper insights into how information flows through different layers and identify any issues or limitations within the model architecture. This helps us better understand the roles and dynamics of the intermediate layers. Templated type-safe hashmap implementation in C using open addressing and linear probing for collision resolution. They are commonly used for: Together, we can overcome anything. Feb 6, 2025 · Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. Feb 5, 2025 · AI models might use deceptive strategies as part of scheming or misaligned behaviour. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. They allow us to understand if the numeric representation Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, Apr 4, 2025 · Developing effective world models is crucial for creating artificial agents that can reason about and navigate complex environments. Connect it right to a compatible Android or iOS device. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP.