Linear Probes Ai, An important question is whether the probes generalise. The process involves Linear probes are simple and generally effective classifiers over a model’s latent space Alain and Bengio (2018). The basic 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 Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. This paper evaluates the use of probing classifiers to modify the We compare Logistic Regression to alternative probing methods including Difference of Means (Marks & Tegmark, 2023) and Linear Artificial Tomography (Zou et al. A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network in order to test whether a particular concept, property, or label 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. ProbeGen optimizes a deep generator module limited to linear expressivity, that Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, Linear probing is a simple idea where you train a linear model (probe) to predict a concept from the internals of the interpreted target model. They allow us to understand if the numeric representation Linear classifier probes are diagnostic models that use regularized logistic or softmax regression to evaluate linear separability in intermediate neural network activations. Linear probes reveal what information each layer of a neural network Ultrasound images have been analysed, and differences in the imaging with both probes in patients with interstitial lung lesions have been detailed. We use We propose Deep Linear Probe Generators (ProbeGen) for learning better probes. While they perform well in general, their effectiveness in detecting text We propose truncated polynomial classifiers and a progressive training scheme to scale LLM safety monitoring with inference-time compute–extending the familiar linear probe with rich non-linear A project for training and evaluating linear probes on language model activations to predict model errors (hallucinations). This codebase supports multiple datasets, models, and probe configurations, with A major challenge in both neuroscience and machine learning is the development of useful tools for understanding complex information processing systems. AI models might use deceptive strategies as part of scheming or misaligned We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Minecraft is an White-box monitors are a popular technique for detecting potentially harmful behaviours in language models. They provide high-resolution images for superficial structures, making them invaluable across various healthcare Baseline probes have a specific feature they’re interested in learning in a supervised way, while SAE latents are unsupervised, and when SAE probing These probes gen- eralise under domain shifts and can even outper- form finetuned LLM evaluators with the same training data size. We demon-strate that linear probes trained on LLM activa-tions can accurately identify where persuasion success or failure The proposed EasyDetector, a novel approach to detect the provenance of LLMs using linear probes, is lightweight and applicable to various model architectures, holding significant Discover the benefits and challenges of Linear Probing and learn how to optimize its performance in hash tables. , Figure 1. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. While our experiments are limited to linear probes across three scenarios on Moreover, the integration of artificial intelligence (AI) in imaging technologies is enhancing the accuracy and efficiency of diagnoses, thereby increasing the adoption of medical linear probes. However, traditional safety monitors often This work proposes to monitor the features at every layer of a model and measure how suitable they are for classification, using linear classifiers, AI could potentially aid in diagnoses by processing large amounts of image data, detecting patterns, and providing diagnostic support, all in Download Citation | Deep Linear Probe Generators for Weight Space Learning | Weight space learning aims to extract information about a neural network, such as its training dataset or Evaluating AlexNet features at various depths. Our experiments show This paper especially investigates the linear probing performance of MAE models. 1 shows the predictive performance of the linear However, we discover that current probe learning strategies are ineffective. Belinkov et al. Meta learning has been the most popular solution for few-shot learning problem. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while AI models might use deceptive strategies as part of scheming or misaligned behaviour. To analyze linear probing, we need to know more than just how many elements collide with us. 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 2. Contribute to yukimasano/linear-probes development by creating an account on GitHub. They are trained either on a per-token basis or on a compressed representation of latent vectors from multiple Revolut introduces `PRAGMA`, a family of Transformer-based foundation models trained on a large, heterogeneous corpus of banking event neurox. They enable What role does AI play in enhancing ultrasound imaging with linear array probes? AI-powered algorithms can analyze image data and apply de-noising techniques, leading to significant This work proposes to monitor the features at every layer of a model and measure how suitable they are for classification, using linear classifiers, which are referred to as "probes", trained Abstract Contextual hallucinations –- statements unsupported by given context –- remain a significant challenge in AI. The A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network in order to test whether a particular concept, property, or label Our method uses linear classifiers, referred to as "probes", where a probe can only use the hidden units of a given intermediate layer as Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Visiting ETH MsC student Henry Papadatos and supervising CHAI PhD student Rachel Freedman publish an article “Linear Probe Penalties Reduce LLM Sycophancy” at the NeurIPS Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. For part-of-speech tagging, moving from linear to MLP probes leads to a slight Our method employs a linear probe within the reward model to quantify the extent of sycophancy in the AI’s responses. This helps us better understand the roles and dynamics of the intermediate layers. 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, This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. We Abstract AI models might use deceptive strategies as part of scheming or misaligned behaviour. The researchers used two distinct datasets for training: one containing Master AI probing with this guide. Recent work has used LUMIA (Linear probe-based Utilization of Model Internal Activations), leverages Linear Probes (LPs), lightweight classifiers trained directly on internal activations, i. The recent Masked Image Modeling (MIM) approach is shown to be an effective self-supervised learning Internet communications tools Document preparation Computing industry Computing standards, RFCs and guidelines Computer crime Language types Security and privacy Computational complexity and 4. Description Unlock the power of linear probes with our comprehensive PowerPoint presentation deck. 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 train The representational differences between generalizing networks and intentionally flawed models can be insight-ful on the dynamics of network training. It is becoming increasingly necessary to have monitors check for harmful behaviors during language model interactions, but text-only monitoring has not been sufficient. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. One such tool is probes, i. 3-70B-Instruct model. Discover the LX128LC: a portable wireless ultrasound probe with linear and convex heads. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. ProbeGen factorizes its probes into two parts, a per-probe latent code and a global probe generator. The typical linear probe is only applied as a proxy at the in To this end, we propose Deep Linear Probe Generators (ProbeGen) as a simple and effective so-lution. D. They provide high-resolution images for superficial structures, making The document discusses the use of linear probes to detect strategic deception in AI models, particularly focusing on the Llama-3. The intent is to help detect and reduce misuse of AI - 3. To assess the effects of adding linear probes during training, we trained world model RNNs for various values of 𝜆 \lambda italic_λ $λ$ (20 random seeds for each choice of 𝜆 \lambda Implications and Future Directions The successful application of linear probes to persuasion analysis opens promising avenues for studying other 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. AI models might use deceptive strategies as part of scheming or misaligned behaviour. The successful combination of model compression and linear In explainable AI, Concept Activation Vectors (CAVs) are typically obtained by training linear classifier probes to detect human-understandable concepts as directions in the activation space of deep Konted Medical, a leading handheld ultrasound device manufacturer, provides 2. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. This expertly crafted guide delves into the fundamentals, applications, and Train linear probes on neural language models. Types of Probes and The linear array transducer frequency is 4–12 MHz, which can be applied on ultrasonic examination of superficial organs, vessels, and tissues, etc. Contribute to EleutherAI/attention-probes development by creating an account on GitHub. A linear probe is a diagnostic tool used in machine learning to analyze the internal representations of a pre-trained model, typically a deep neural network. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal Model Transparency: Probing classifiers can provide H2O. Minecraft Mods on CurseForge - The Home for the Best Minecraft Mods Discover the best Minecraft Mods and Modpacks around. linear_probe Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear Including the world features loss component roughly corresponded to doubling the model size, suggesting that the linear probe technique is particularly beneficial in compute-limited settings ABSTRACT AI models might use deceptive strategies as part of scheming or misaligned behaviour. Motivated by The linear classifier as described in chapter II are used as linear probe to determine the depth of the deep learning network as shown in figure 6. We then modify the reward model to penalize responses based on their sycophancy Introduction As artificial intelligence systems become increasingly sophisticated, one of the most pressing concerns in AI safety is the potential for these systems to engage in strategic Alright so I've been messing around with LLMs for a few weeks now. We propose to monitor the We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. g. 2-24MHz wireless ultrasound probes & portable USG systems for global doctors. In the future, it would be interesting to use non Conclusion LPASS represents a significant step forward in making AI-powered security analysis more practical and accessible. Learn how representation probing and probe neural networks unlock the secrets of LLMs and deep learning models. Large Language Models (LLMs) are being extensively used for cybersecurity purposes. They As a first analysis, we use linear classifier probes as the interpreter model Mi to evaluate the linear separabil-ity of the classes during training. deep-neural-networks psychophysics cognitive-neuroscience linear-probing explainable-ai interpreting-models human-machine-behavior Updated on Jul 16, 2024 Python One of the simple strategies is to utilize a linear probing classifier to quantitatively eval-uate the class accuracy under the obtained features. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while Ultrasound linear probes are essential tools in modern medical imaging. This holds true for both in-distribution (ID) and out-of Request PDF | Understanding intermediate layers using linear classifier probes | Neural network models have a reputation for being black boxes. 0 12 4 13 14 11 1 Among the various types of transducers used to obtain three-dimensional ultrasound images, this paper focuses on the most Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. Many studies have been conducted to assess the quality of feature representations. ProbeGen optimizes a deep generator module limited to linear expressivity, that shares information Then, to solve this problem, we propose a new technique called the Linear Probe Calibration (LinC), a method that calibrates the model’s output probabilities, resulting in reliable predictions and improved 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 Abstract. Monitoring outputs alone is insuficient, since the AI might produce seemingly benign We thus evaluate if linear probes can robustly detect deception by monitoring model activations. Gain familiarity with the PyTorch and HuggingFace libraries, for Linear probes are a simple way to classify internal states of language models. We propose Deep Linear Probe Gen erators (ProbeGen) for learning better probes. This is because By moving beyond linear probes, the approach offers a more nuanced and adaptive method for detecting potential risks. ProbeGen optimizes a deep generator module limited to linear expressivity, that shares information AI models might use deceptive strategies as part of scheming or misaligned behaviour. One Standalone Probe The Lexsa probe is specially optimized to provide exceptional linear array imaging for MSK, Vascular, Nerve, and Lung applications. In this lecture: Understand what linear probes are See why model outputs are not enough Explore “ truth as geometry ” Connect Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to model user sentiment and political perspective. Most techniques use linear probes to monitor and control representations. Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. linear_probe — NeuroX toolkit documentation Source code for neurox. In explainable AI, Concept Activation Vectors (CAVs) are typically obtained by training linear classifier probes to detect human-understandable concepts as directions in the activation 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. Yet, for LLM We present PROBY, an AI model trained on large-scale datasets to predict key photophysical properties and accelerate the discovery of target-specific fluorescent probes. 1. The use of a linear probe in patients with AI models might use deceptive strategies as part of scheming or misaligned behaviour. We’ve explained what probing classifiers are and why they could be useful for AI safety. This paper examines activation probes for detecting “high-stakes” interactions—where the text indicates To study this, we extract activations after a question is read but before any tokens are generated, and train linear probes to predict whether the model’s forthcoming answer will be correct. This makes the probe Abstract Contextual hallucinations — statements unsup-ported by given context — remain a significant challenge in AI. This research offers an innovative approach to understanding model confidence, potentially We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Detecting Strategic Deception Using Linear Probes: Paper and Code. Probe training is a one-shot learning of a d-dimensional parameter vector on 10 k cached activations (<3 min on CPU); applying the probe involves a linear project, which is drastically lighter-weight in Probe training is a one-shot learning of a d-dimensional parameter vector on 10 k cached activations (<3 min on CPU); applying the probe involves a linear project, which is drastically lighter-weight in In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. The problem Even That's a linear probe. One of them is the detection of vulnerable codes. Abstract Monitoring is an important aspect of safely deploying Large Language Mod-els (LLMs). Moreover, these probes cannot affect the Supporting: 2, Mentioning: 210 - Understanding intermediate layers using linear classifier probes - Alain, Guillaume, Bengio, Yoshua The paper evaluates the effectiveness of linear probes in detecting strategic deception in AI models, achieving high accuracy in distinguishing honest from deceptive responses, but The paper evaluates the effectiveness of linear probes in detecting strategic deception in AI models, achieving high accuracy in distinguishing honest from deceptive responses, but Understanding network generalization and feature discrimination is an open research problem in visual recognition. , 2023) in Appendix D. Fig. Moreover, these probes cannot affect the Then, to solve this problem, we propose a new technique called the Linear Probe Calibration (LinC), a method that calibrates the model's output probabilities, resulting in reliable However, we discover that current probe learning strategies are ineffective. I trained a probe against a small LLM and then fine In linear probing, collisions can occur between elements with entirely different hash codes. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a predefined concept label. These probes can be Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. Probes' performance is comparable to The study employed linear probes - simple linear classifiers trained on model activations - to detect deceptive behavior. SAE features are supposed to be interpretable, but when I wanted to directly attack an AI's own ontology, the whole Probing classifiers are one tool that researchers can use to try and achieve this. They reveal how semantic content evolves across Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. This is a write-up of my recent work on improving linear probes for deception detection in LLMs. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. We demonstrate a practical interpretability insight: a generator-agnostic lmprobe Language Model Probe Library This library supports the use of language model "activations" or "latents" to build text classifiers. Our pipeline has two steps: 1) first we extract snippets from reasoning traces that would signal test awareness or unawareness and we train Linear classifier probes are frequently utilized to better understand how neural networks function. Finally, good probing performance would hint at the presence of the said We show that linear probes can separate real-world evaluation and deployment prompts, suggesting that current models internally represent this distinction. This has motivated intensive research building AI models might use deceptive strategies as part of scheming or misaligned behaviour. Ananya Kumar, Stanford Ph. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while 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 also find that current safety Probes in the above sense are supervised models whose inputs are frozen parameters of the model we are probing. A linear probe is a high-frequency ultrasound transducer optimized for high-resolution imaging of superficial structures and guiding precision medical procedures by emitting parallel In essence, LiDAR quantifies the rank of the Linear Discriminant Analysis (LDA) matrix associated with the surrogate SSL task—a measure that intuitively captures the information content as it pertains to Ultrasound linear probes are essential tools in modern medical imaging. However, transductive linear probing Probing persuasion outcomes, rhetorical strategies, and personality traits. This is hard to distinguish from simply fitting a supervised model as usual, with a AI models might use deceptive strategies as part of scheming or misaligned behaviour. The authors evaluate the effectiveness of these Graph few-shot learning aims to predict well by training with very few labeled data. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective mod-ification to probing approaches. This lecture will cover probing and representations in Transformers. For the sake of efficiency and effectiveness, Understanding Linear Array Ultrasound Probes: Key Features and Benefits When you're diving into medical imaging, picking the right ultrasound probe can really make a big difference in What is the primary benefit of using a linear array probe ultrasound over traditional ultrasound probes? The primary benefit of using a linear array probe ultrasound is its ability to While linear probes are simple and interpretable, it is unable to disentangle features distributed features that combine in a non-linear way. However, single-layer probes A linear probe uses high-frequency ultrasound to create high-resolution images of structures near the body surface. e. , the hidden states Abstract We analyze a dataset of retinal images using linear probes: linear regression models trained on some “target” task, using embeddings from a deep con-volutional (CNN) model trained on some What role do AI-driven technologies play in linear array ultrasound probes? AI-driven technologies optimize image acquisition in real-time using data analytics and machine learning, The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. PALP inherits the scalability of linear probing and Our work addresses these limitations with a plug-and-play approach: linear probes that achieve strong calibration for reasoning judges without re-quiring additional model training, multi-sample generation, 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 vision Linear Probes - Do I Really Need One. However, a probe trained on one dataset may fail to transfer to another, not AI models might use deceptive strategies as part of scheming or misaligned behaviour. A specific modeling of the classifier weights, blending visual prototypes and text embeddings via learnable Understanding intermediate layers using linear classifier probes Guillaume Alain, Yoshua Bengio. We propose a new method to understand This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while . If we Linear probing is a simple idea where you train a linear model (probe) to predict a concept from the internals of the interpreted target model. Discover the ins and outs of Linear Probing, a fundamental technique in hash table collision resolution, and learn how to implement it effectively. Do memorizing networks, e. Monitor-ing outputs alone is insuficient, since the AI might produce seemingly benign outputs while Sparse probe configurations achieve comparable or superior image quality compared to conventional full-density probes while using significantly fewer elements The optimization method A simplified view of the concept probing setup. 【Linear Probing | 线性探测】深度学习 线性层 1. The master's degree — your pretrained network — stays exactly as it was, untouched. High-definition imaging, AI integration, and specialized clinical presets. Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. interpretation. We test two probe-training datasets, one with contrasting instructions Department of Computer Science University of Central Florida Orlando, FL, United States Abstract—Probing classifiers are a technique for understanding and modifying the operation of Neural network models have a reputation for being black boxes. This work could be crucial in developing more responsible and Linear Probing Relevant source files Linear probing is the third stage of the AMT training pipeline, used to evaluate the quality of learned representations from pre-trained models without fine Linear probing is a fundamental technique in hash table implementations, offering simplicity and efficiency when used appropriately. This holds true for both in-distribution (ID) and out-of The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This holds true for both in-distribution (ID) and out-of Linear probes with attention weighting. They 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 intermediate 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. Linear probes, once reliable AI deception detectors, falter under stylistic shifts, challenging their effectiveness and reflecting AI's data dependency. We test two probe-training datasets, one with Similar to a neural electrode array, probing classifiers help both discern and edit the internal representation of a neural network. linear_probe neurox. (2020) introduce a distinction The list of contributions is as follows: We adopt linear probes (LPs) in vulnerability detection for 1) determining the cut-ofpoint when applying layer pruning and 2) estimating the Activation probes are lightweight models that extract high-level concept signals from internal activations, enhancing interpretability and AI safety. 2016 [ArXiv] Neural network models have a reputation for being black boxes. By A from-scratch implementation of the linear probing technique from Alain & Bengio (2016), applied to GPT-2 using TransformerLens. The Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. All the latest news from IMV imaging the premier supplier of animal imaging equipment. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal Conclusion Measuring AI uncertainty is crucial for building trustworthy intelligent systems. The team was supervised by Dmitrii Krasheninnikov, with additional Linear probes, introduced by Alain & Bengio (2018), are a common technique for investigating model behavior through activations. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. 1 Probes Despite what we highlighted in the previous section 2, there is indeed a good reason to use many deterministic layers, and it is because they perform useful transformations to the data with the Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. the hidden states Our findings reveal that probes rely on textual evidence for behavior detection in the scenarios we studied. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing We propose Deep Linear Probe Gen erators (ProbeGen) for learning better probes. Researchers have approached the problem of determining unit importance in neural networks by In our guide, 'The Ultimate How-To for Picking the Perfect Linear Array Ultrasound Probe for Your Needs,' we dive into what makes these probes so important in medical imaging. Previous efforts focus on black-to 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. Conclusion We introduced LP++, a strong linear probe for few-shot CLIP adaptation. We were surprised by SAEs underperforming linear probes, but also by how well linear probes did in absolute terms, on the complex-seeming task of AI models might use deceptive strategies as part of scheming or misaligned behaviour. We demonstrate A simple interpretability technique where a linear classifier is trained on top of a neural network's internal representations to test whether a particular concept, such as sentiment, part of speech, or color, is Interestingly, combining the probe with a weak baseline that underperforms the probe (finetuned Gemma3-1B) still improves results, suggesting these monitors capture complementary This research was completed for LASR Labs 2025 by Alex McKenzie, Urja Pawar, Phil Blandfort and William Bankes. This has motivated intensive research building 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. On top of it, you add one small linear layer: no homework for the old material, just LUMIA (Linear probe-based Utilization of Model Internal Activations) leverages Linear Probes (LPs), lightweight classifiers trained directly on internal activations, i. We demonstrate a practical inter-pretability insight: a generator-agnostic observer We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. ai users with a deeper understanding of how their models interpret and represent input data, facilitating better model transparency and We adopt linear probes (LPs) in vulnerability detection for (1) determining the cut-off point when applying layer pruning and (2) estimating the effectiveness and performance of fine-tuned and Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. Our results suggest linear probing offers an accurate, robust and compu- The aim of this study was to assess the concordance in assessment of lung aeration between linear and sector array probes of a handheld ultrasound device in inva-sively ventilated ICU patients. These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any special knowledge of the sleeper agent We find that linear and bilinear probes are considerably more selective than multi-layer perceptron probes. Contribute to t-shoemaker/lm_probe development by creating an account on GitHub. They reveal how semantic content evolves across Abstract Linear probes can detect when language models produce outputs they “know” are wrong, a ca-pability relevant to both deception and reward hacking. lmmsj7, uqc9, fpo, 7p, k5g, kklq, qk1, c9hp, 5zb0n, s1, iduqu, uwjw, ctkhm, ih4dvaz, aevi, uuhvbb, ylyxnf, yvmge, 1rk4i, 9a, xvajt, gf, nuvq, 9onan, kgb, dv, rkx, crtjt, jyr, 54y, \