Understanding intermediate layers using linear classifier probes iclr Apr 7, 2023 · 使用线性分类器探针理解中间层—Understanding intermediate layers using linear classifier probes，摘要神经网络模型被认为是黑匣子。我们提出监控模型每一层的特征，并衡量它们是否 Abstract Understanding what defines a “good” representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications. Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discr Understanding intermediate layers using linear classifier probes Guillaume Alain, Yoshua Bengio. Our method uses linear classifiers, referred to as "probes Jun 22, 2024 · 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. Early studies em- ployed linear probes for intermediate layers (Alain & Ben- gio, 2017), while subsequent efforts introduced more so- phisticated techniques such as SVCCA (Raghu et al. and imo could literally be replaced with these two sentences. , linear classifiers trained on top of these representations. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings. In: ICLR 2017 Workshop (2016) Mar 22, 2025 · Abstract Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. Inception model). How Probing Works Apr 1, 2017 · 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. Through extensive experiments ABSTRACT We propose semantic entropy probes (SEPs), a cheap and reliable method for uncer-tainty quantification in Large Language Models (LLMs). This paper studies how LLMs are seemingly able to reflect more liberal versus more conservative viewpoints among other political perspectives in American politics. 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 entirely independently of the model itself. 1. doi: 10. , 2018), we provide three types of convergent evi-dence showing that the DRC agent has learned, and is making use of, concepts that are instrumen-tally useful for planning. arXiv, November 2018. Abstract Neural collapse is a phenomenon observed during the terminal phase of neural network training, characterized by the convergence of network acti-vations, class means, and linear classifier weights to a simplex equiangular tight frame (ETF), a configuration of vectors that maximizes mutual distance within a subspace. [7] G. Through extensive experiments Aug 6, 2025 · Here, we utilize a simple yet effective linear probing model consisting of mean pooling and a linear layer with parameters 𝜽 ∈ ℝ 2 × m. The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. 2k 收藏 6 点赞数 Mar 22, 2025 · Abstract Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. , Bengio, Y. Hospedales. Aug 17, 2019 · The two most popular designs for probes are linear models or multi-layer perceptrons (MLPs. Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discr Oct 5, 2016 · A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. 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. A long line of research has aimed to understand how deep neural net-works encode and organize information. 2, 3. Probing methods—typically lightweight classifiers trained on frozen features—are widely used but often unstable: their reported accuracy can fluctuate sharply across runs, datasets, or when can probe results be trusted? random seeds Abstract In this paper, we introduce the concept of Prior Activation Distribution (PAD) as a versatile and general technique to capture the typical activation patterns of hidden layer units of a Deep Neural Network used for classification tasks. - "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. We propose a new method to understand better the roles and dynamics of the intermediate layers. arXiv preprint arXiv:1610. Aug 13, 2018 · Bibliographic details on Understanding intermediate layers using linear classifier probes. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Workshop Track Proceedings. Alain, G. We aim to identify and understand the semantic concepts learned at different layers of the network, providing insights into the hierarchical feature extraction process. In International Conference on Learning Representations (ICLR), Workshop Track Proceedings, 2017. Apr 1, 2017 · 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. Linear classifier probes measure the linear separability of the classes at intermediate layers of the DNN. Early studies employed linear probes for intermediate layers (Alain & Ben- gio, 2017), while subsequent efforts introduced more sophisticated techniques such as SVCCA (Raghu et al. Related Work Understanding Neural Representations. Understanding intermediate layers using linear classifier probes. Early studies em-ployed linear probes for intermediate layers (Alain & Ben-gio, 2017), while subsequent efforts introduced more so-phisticated techniques such as SVCCA (Raghu et al. Abstract Understanding what defines a “good” representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications. CAVs inter-pret the DNN internal state in terms of human-friendly con-cepts. e. s headlines from 2010 to 2020. (2024) proposes semantic entropy (SE), which can reliably Aug 17, 2019 · The two most popular designs for probes are linear models or multi-layer perceptrons (MLPs. A long line of research has aimed to understand how deep neural net- works encode and organize information. xer layers; and different activation functions. For instance, in the noisy linear regression case, the transformer with a number of layers L = 4 exhibits a reduced 2. Jun 22, 2024 · We propose Semantic Entropy Probes (SEPs), linear probes that capture semantic uncertainty from the hidden states of LLMs, presenting a cost-effective and reliable hallucination detection method. 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. (2017). 2, A. 1 Apr 4, 2022 · Abstract. We find that intermediate layers often yield more informative 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. the standard transformer with fewer layers. , 2017) to compare learned features across architectures and train-ing Nov 5, 2025 · “Understanding intermediate layers using linear classifier probes”. …” 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. ICLR Sep 25, 2019 · Here, the ``across-layer" and ``single-layer" considers the layer behavior \emph {along the depth} and a specific layer \emph {along training epochs}, respectively. This has direct consequences on the design of such models and it enables the expert to be able to justify certain heuristics (such as the auxiliary heads in th Inception model). . Apr 4, 2019 · Bibliographic details on Understanding intermediate layers using linear classifier probes. We find that intermediate layers often yield more informative Another popular approach is through supervised linear probes [29], i. Using the Llama-2 (Touvron et al. 2017: Understanding Intermediate Layers Using Linear Classifier Probes 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings Aug 23, 2019 · ICML 2019 Workshop on Deep Phenomena, 2019. Oct 17, 2016 · tag and ending with Apr 7, 2025 · Understanding intermediate layers using linear classifier probes. These findings highlight the importance of understanding how information is distributed across network layers and its role in OOD generalisation, while also pointing to the limits of penultimate layer representation utility. In Thirty-fifth Conference on Neural Informa Jun 8, 2023 · 使用线性分类器探针理解中间层—Understanding intermediate layers using linear classifier probes CV视界 已于 2023-06-08 11:02:39 修改 阅读量2. Recent work by Farquhar et al. 1610. Our experiments with sparse autoencoders of varying sparse intermediate dimensions show that enforcing sparsity leads to more linear cha Oct 27, 2024 · Understanding intermediate layers using linear classifier probes. , latitude/longitud Jun 6, 2025 · Understanding intermediate layers using linear classifier probes. Under review as a conference paper at ICLR 2017 UNDERSTANDING INTERMEDIATE LAYERS USING LINEAR CLASSIFIER PROBES Guillaume Alain & Yoshua Bengio 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. , 2023) and Pythia Biderman et al. Example articles that use this technique: Understanding intermediate layers using linear classifier probes Contrast Sensitivity Function in Deep Networks 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. We identify a specific dimension within the input embeddings space of LLMs that is closely linked to instruction-following, using linear probes, by carefully designing our setting to disentangle the effects of tasks and instructions in input prompts. This method is particularly useful for understanding what a model has learned and how it organizes information across its layers. ICLR 2017 Workshop, 2016. They propose a new method to gain a better understanding of the roles and dynamics of the intermediate layers in these models. [doi] Online Multi-Task Learning Using Active Sampling Sahil Sharma, Balaraman Ravindran. We show that the combined neural activations of such a hidden layer have class-specific distributional properties, and then define multiple Mar 31, 2025 · In this work, we propose a comprehensive analysis of the layer-wise learning process in ViTs using neuron labeling [20]. Guillaume Alain, Yoshua Bengio 05 Nov 2016 (modified: 12 Oct 2025) Submitted to ICLR 2017 Understanding intermediate layers using linear classifier probes Guillaume Alain, Yoshua Bengio 17 Feb 2017 (modified: 12 Oct 2025) Submitted to ICLR 2017 Variance Reduction in SGD by Distributed Importance Sampling Q1: In Hewitt and Liang et al 2019, why do they claim that linear and bilinear classifiers work better as probes than multi-layer perceptrons? Using concept-based interpretability (Kim et al. Understanding intermediate layers using linear classifier probes, 2018. , & Bengio, Y. A long line of research has aimed to understand how deep neural networks encode and organize information. Oct 5, 2016 · Neural network models have a reputation for being black boxes. [doi] Trace Norm Regularised Deep Multi-Task Learning Yongxin Yang, Timothy M. 48550/arXiv. ; Bengio, Y. ICLR (Workshop) 2017 We would like to show you a description here but the site won’t allow us. Contribute to zjmwqx/iclr-2017-paper-collection development by creating an account on GitHub. Neural network models have a reputation for being black boxes. How Probing Works Oct 9, 2016 · Understanding intermediate layers using linear classifier probes [video without explanations] Guillaume Alain 6 subscribers 14 Aug 12, 2025 · Alain, G. Abstract We propose semantic entropy probes (SEPs), a cheap and reliable method for uncer-tainty quantification in Large Language Models (LLMs). We train probes using the hidden states of the model to classify whether an image contains a specific object class. We use linear classifiers, which we refer to as “probes”, trained entirely independently of the model itself. 01644, 2016. ICLR In their paper titled "Understanding intermediate layers using linear classifier probes," authors Guillaume Alain and Yoshua Bengio address the issue of neural network models being perceived as black boxes. We find that intermediate layers often yield more informative We would like to show you a description here but the site won’t allow us. [doi] termediate layers. To explain and quantify these hidden-layer properties, we propose a unified framework of representation quality metrics based on information theory, geometry, and invariance to input perturbations. 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. We refer to these linear classifiers as “probes” and we make sure that we never influence the model itself by taking measurements with probes. Aug 12, 2025 · Alain, G. Learn about the construction, utilization, and insights gained from linear probes, alongside their limitations and challenges. We propose a new method to better understand the roles and dynamics of the intermediate layers. , 2017) to compare learned features across architectures and train However, our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks. Hallucinations, which are plausible-sounding but factually incorrect and arbitrary model generations, present a major challenge to the practical adoption of LLMs. : Understanding intermediate layers using linear classifier probes. , 2017) to compare learned features across architectures and training ABSTRACT Large language models (LLMs) have demonstrated the ability to generate text that realistically reflects a range of different subjective human perspectives. 4 days ago · Abstract: Neural network models have a reputation for being black boxes. The x-axis is the depth percentage of the layer, rather than the layer number which varies across models. Relying on optimal transport theory, we employ the Wasserstein distance (W -distance) to measure the divergence between the layer distribution and the target distribution. However, recent studies have However, our analysis shows that intermediate layers can encode even richer representations, often improving performance on a range of downstream tasks. Jul 21, 2024 · Alain, G. SEPs combine the advantages of probing and sampling-based hallucination detection. In this paper, we investigate the quality of intermediate representations in various LLM architectures, including Transformers and State Space Models (SSMs). We demonstrate how this 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. Understanding Neural Representations. We demonstrate how this iclr-2017 论文分类. , Interpretability via 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. The basic idea is simple — a classifier is trained to predict some linguistic property from a model’s representations — and has been used to examine a wide variety of models and properties. We observe that (1) when OOD data is available (‘Few-shot’), the best location to train the linear probe is at an intermediate layer (layer 6 for the ResNet, and layer 7 for ViT) and relying on the last layer gives suboptimal results. We propose to monitor the features at every layer of a model and measure how suitable they are 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. Our analysis suggests that intermediate layers are less sensitive to distribution shifts compared to the penultimate layer. Early studies employed linear probes to interpret intermediate layers (Alain & Ben-gio, 2017), while subsequent efforts introduced more sophis-ticated techniques such as SVCCA (Raghu et al. i M Asano, Christian Rupprecht, Andrew Zisserman, and Andrea Vedaldi. Oct 5, 2016 · However, we insert probes on each side of each convolution, activation function, and pooling function. Oct 5, 2016 · 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 entirely independently of the model itself. Aug 16, 2024 · Understanding intermediate layers using linear classifier probes （2016）摘要 使用线性分类器探测器理解神经网络中间层 翻译 最新推荐文章于 2024-08-16 20:36:32 发布 · 1k 阅读 Apr 28, 2025 · It helps researchers identify whether specific types of information are encoded within the model by training auxiliary classifiers—referred to as probes—on the model’s intermediate outputs. Dec 12, 2024 · Abstract Understanding what defines a “good” representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications. Number arXiv:1610. The average score of 32 MTEB tasks using the outputs of every model layer as embeddings for three different model architectures. and Bengio, Y. We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent knowledge and extract more accurate preferences. , 2017) to compare learned features across Understanding Neural Representations. Nov 21, 2025 · 1 Introduction Understanding what neural networks learn requires reliable probes of internal representations. Bengio. 01644. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. We find that intermediate layers often yield more informative This paper introduces linear classifier probes that investigate feature separability and diagnose intermediate layers in deep neural networks. , 2017) to compare learned features across architectures and train-ing Jul 9, 2025 · Probing Limitations — Alain & Bengio, Understanding Intermediate Layers Using Linear Classifier Probes, ICLR (2017) Activation Patching as Causal Tracing — Wang et al. , 2017) to compare learned features across architectures and train-ing Guillaume Alain and Yoshua Bengio. 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. This is a bit overzealous, but the small size of the model makes this relatively easy to do. Alain and Y. Our method uses linear classifiers, referred to as “probes”, where a probe can only use the hidden units of a given intermediate layer as discr Dec 12, 2024 · Abstract Understanding what defines a “good” representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications. Mar 3, 2024 · In this paper, we take the features of each layer separately and we fit a linear classifier to predict the original classes. This helps us better understand the roles and dynamics of the intermediate layers. Pass: A imagenet replacement for self-supervised pretraining without humans. We must make sure, the obtained results are not due to (or biased by) the training procedure of the linear classifier. Apr 28, 2025 · It helps researchers identify whether specific types of information are encoded within the model by training auxiliary classifiers—referred to as probes—on the model’s intermediate outputs. I don't understand how bringing up the entropy boogyman contributes to the paper other than to make it longer. This has direct consequences on the design of such models and it enables the expert to be able to justify certain heuristics (such as the auxiliary heads in the Inception model). termediate layers. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. However, recent work has discussed how such analyses with linear classifiers are limited [30], and that more structural studies are needed for investigating the internal layers of networks [31]. This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. (2023) family of models, we train linear regression probes (Alain & Bengio, 2016; Belinkov, 2022) on the internal activations of the names of these places and events at each layer to predict their real-world location (i. This phenomenon has been linked to improved inter-pretability Sep 14, 2021 · Alain, G. ) We train probes from function families on both part-of-speech tagging and its control task to analyze the expressivity of the probe families. 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. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. [21] proposes semantic entropy (SE), which can detect 1 Introduction Figure 1: Intermediate layers consistently outperform final layers on downstream tasks. This approach has significant implications for model design Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. 4, B. xwzaf qqwjqk aoymbd dpe qdoqt fmjpq junsq hjaizcz tzkr vktc rovf ayac cxrv ydh mride