Probing classifier. However, recent studies have demonstrated .

Probing classifier In other words, we will only use ImageGPT to produce fixed features X of images, on which we will then fit a linear classifier together with the labels y. Our approach extracts embeddings from the last hidden layer of selected VLMs and inputs them into a neural probing classifier for multi-class veracity classification. 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. The document reviews the probing classifiers framework, a method for interpreting deep neural network models in natural language processing by training classifiers to predict linguistic properties from model representations. However, recent studies have Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. However, recent studies have demonstrated This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Even under the most favorable conditions for learning a probing classifier when a concept's relevant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc Sep 18, 2024 · Probing with Logistic Regression: A logistic regression classifier was trained on top of the extracted embeddings from both models to evaluate their utility for the sentiment classification task. However, recent studies have demonstrated 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 4 days ago · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. One classifier performs token-level entity typing using hidden states at a single layer, while a second classifier detects spans based on attention weights. Traditional probing methods like logistic regression often face accuracy limitations, making it Sep 11, 2020 · Edge probing decomposes structured-prediction tasks into a common format, where a probing classifier receives a text span (or two spans) from the sentence and must predict a label such as a constituent or relation type, etc. Abstract Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Nov 13, 2025 · The probing classifier (also named Auxiliary Prediction Task and Diagnostic Classifier) is a rule-based post hoc explanation method from NLP (Giulianelli et al. We demonstrate how this Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The basic idea is simple —a Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies This study evaluates the effectiveness of Vision Language Models (VLMs) in representing and utilizing multimodal content for fact-checking. Here we define a simple linear classifier, which takes a word representation as input and applies a linear transformation to map it to the label space. This linear probe does not affect the training procedure of the model. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. In this Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The probing technique refers to methods used to understand the knowledge that LLMs such as BERT have captured. Each dataset is divided into a training and a test set with an 8:2 ratio, and we adhered to the standard procedure for probing classifiers in LLMs, extracting feature representations from the final hidden states at each layer of the LLMs to serve as input to Feb 24, 2021 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. 2019] and T5 [Raffel et al. (Adi et al. However, recent studies have demonstrated Oct 31, 2025 · Our probing experiments reveal that LLM architectures encode CoT differently across representation types and layers, with simple linear classifiers achieving strong performance. Even under the most favorable conditions for learning a probing classifier when a concept's relevant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc May 31, 2025 · Understanding Graph Neural Networks Through Probing Classifiers New methods shed light on GNNs and their properties. , 2020c; Arjovsky et al. However, recent studies have demonstrated Sep 20, 2025 · 【Linear Probing | 线性探测】深度学习 线性层 1. Yanai Elazar works on Oct 1, 2021 · Many scientific fields now use machine-learning tools to assist with complex classification tasks. Yanai Elazar is postdoctoral researcher on the AllenNLP team at AI2. Recently, linear probes [3] have been used to evalu-ate feature generalization in self-supervised visual represen-tation learning. The basic idea is simple — a classifier is trained to pre… Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. , 2021) and POS tagging (Kunz and Kuhlmann, 2021). However, recent studies have 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. However, recent studies have demonstrated 2 days ago · Embedded Named Entity Recognition using Probing Classifiers. . Abstract: Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Feb 24, 2021 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. However, recent studies have demonstrated Dec 6, 2024 · RQ3: Evaluating probing classifiers: How does a probing neural classifier compare to baseline models in the context of the fact-checking task? This study proposes a probing classifier that involves extracting the last hidden layer’s representation and using it as input for a neural network. Probes in the above sense are supervised Sep 19, 2024 · Probing September 19, 2024 • Rahul Chowdhury, Ritik Bompilwar Who are the paper authors? The authors of the papers of today's discussion are mainly Kenneth Li, PhD student at Harvard University, and Dr. These classifiers aim to understand how a model processes and encodes different aspects of input data, such as syntax, semantics, and other linguistic features. , 2019; Sagawa et al. However Even under the most favorable conditions for learning a probing classifier when a concept’s rel-evant features in representation space alone can provide 100% accuracy, we prove that a probing classifier is likely to use non-concept features and thus post-hoc or adversarial methods will fail to remove the concept correctly. The basic idea behind classifier-based probing is to train a shallow classifier on top of the pre-trained or fine-tuned language models such as BERT [Devlin et al. Traditional probing methods like logistic regression often face accuracy limitations, making it 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 networks in which a smaller classifier is trained to use the model’s internal repre-sentation to learn a probing task. The working process of the probing classifier in this paper is shown in Figure 2. Figure 1: Illustration of the proposed approach for named entity recognition using probing classifiers. 4Note that the term probing is also used for analyses con- ductedinanin-contextlearningsetting(seeforexampleEpure and Hennequin(2022)), a parameter-free technique which dif- fers from the use probing classiers. Classifiers using Feature Activations can be Competitive with Raw Activations Abstract Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. As previous work has argued (Tsipras et al. sentence length predict the length (number of tokens) of the input sentence s probe network classifier sent. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. What are Probing Classifiers? Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. The reason this can work is that the first step learns a reasonably good classifier, and so now, in the fine-tuning step, you don’t need to change the linear classifier much. Learn about the construction, utilization, and insights gained from linear probes, alongside their limitations and challenges. As I understand, you employ a logistic regression model for the probing classifier, and you compare the model output with the ground truth to assess correctness, which is then used to We show that the auxiliary classifier cannot be a reliable signal on whether the representation includes features that are causally derived from the concept. Dec 6, 2024 · RQ3: Evaluating probing classifiers: How does a probing neural classifier compare to baseline models in the context of the fact-checking task? This study proposes a probing classifier that involves extracting the last hidden layer’s representation and using it as input for a neural network. However, recent studies have The reason is the methods' reliance on a probing classifier as a proxy for the concept. This work advances the interpretability of language models by moving beyond simple input-output analysis toward mapping internal reasoning geography. By probing a pre-trained model's internal representations, researchers and data Apr 4, 2022 · Abstract. They allow us to understand if the numeric representation 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). A key advantage of probing classifiers is their ability to assess how well the pre-trained model has captured linguistic proper-ties. Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. 原理 训练后，要评价模型的好坏，通过将最后的一层替换成线性层。 Jul 8, 2022 · The reason is the methods' reliance on a probing classifier as a proxy for the concept. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17830–17850, Miami, Florida, USA. Oct 4, 2021 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. However, recent studies have demonstrated Attention weights: Probe classifiers are built on top of attention weights to discover if there is an underlying linguistic phenomenon in attention weights patterns. This paper explores the use of gradient boosting decision trees on the hidden layers of transformer neural networks for probing classifiers. 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. Abstract Read online AbstractProbing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. , 2018; Veldhoen et al. Abstract Classifiers trained on auxiliary probing tasks are a popular tool to analyze the representations learned by neural sentence encoders such as BERT and ELMo. While many authors are aware of the difficulty to distinguish between “extracting the linguistic structure encoded in the representations” and “learning the probing task,” the validity of probing methods calls for further Nov 2, 2025 · By training classifiers to probe hidden layers systematically, researchers can map where different types of thinking occur within neural networks. An early usage of probing tasks can be found in Shi et. Classifier-Based Probing. 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. Similar to a neural electrode array, probing classifiers help both discern and edit A probing experiment also requires a probing model, also known as an auxiliary classifier. Linear probing means fitting a linear classifier (like logistic regression) on the fixed features of a pre-trained model. 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. , 2017) Feed-forward NN trained from scratch RQ3: Evaluating probing classifiers: How does a probing neural classifier compare to baseline models in the context of the fact-checking task? This study proposes a probing classifier that in-volves extracting the last hidden layer’s representa-tion and using it as input for a neural network. , 2016). Kenneth Li is working on LLM dialogues and interpretability for alignment of LLMs. In neuroscience, automatic classifiers may be usefu… Dec 6, 2024 · View a PDF of the paper titled Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies, by Recep Firat Cekinel and 2 other authors 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. Instead of fine-tuning, probing classifiers are trained on the representations of a pre-trained model (Kunz and Kuhlmann, 2020) to predict lin-guistic features such as dependency parsing (Adel-mann et al. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. Dec 17, 2023 · This paper explores the use of gradient boosting decision trees on the hidden layers of transformer neural networks for probing classifiers. The study aims to improve the process of understanding and interpreting the capabilities of large language models (LLMs) in capturing syntactic features. However, recent studies have Nov 20, 2024 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. pdf), Text File (. However, recent studies have Probing - Free download as PDF File (. , 2018; Hupkes et al. Train simple classifier probes on hidden states to test for encoded linguistic information. from per-token embeddings for tokens within those target spans. É Probes cannot tell us about whether the information that we identify has any causal relationship with the target model’s behavior. Dr. Final section: unsupervised probes. Both predictions are aggregated into span-level entity predictions. View recent discussion. And so the features change a lot less. , 2020; Belinkov, 2022), if the representation features causally derived from the concept are not predictive enough, the probing classifier for the Using raw-activations for the classifier is a strong baseline and may be preferable for applications where classifier performance is more important than the specific benefits of using 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. 2020]. Nov 16, 2019 · The probing task is designed in such a way to isolate some linguistic phenomena and if the probing classifier performs well on the probing task we infer that the system has encoded the linguistic phenomena in question. May 31, 2025 ― 7 min read Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. However, recent studies have demonstrated Apr 5, 2023 · First you linear probe—you first train a linear classifier on top of the representations, and then you fine-tune the entire model. 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. However, recent studies have demonstrated Feb 24, 2021 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Oct 5, 2016 · Neural network models have a reputation for being black boxes. This helps us better understand the roles and dynamics of the intermediate layers. Jan 31, 2025 · For our probing analysis, we selected linear classifier probing for our experiments. txt) or read online for free. al (2016) Does String-Based Neural MT Learn Source Syntax? Oct 21, 2024 · Thanks for your work!I have a question regarding the probing classifier mentioned in the supplementary material. The basic idea is simple -- a The linear probe is a linear classifier taking layer activations as inputs and measuring the discriminability of the networks. However Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. repr. 2 days ago · Furthermore we propose a probing classifier based solution using VLMs. Feb 17, 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.