Probing classifiers. However, recent studies have demonstrated .
Probing classifiers Apr 28, 2025 · How Probing Works Probing involves training supervised classifiers, typically simple ones like linear probes, to predict specific properties from the internal representations of a model. Dec 16, 2024 · Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, recent studies have demonstrated We show that the auxiliary classifier cannot be a reliable signal on whether the representation includes features that are causally derived from the concept. , 2016). We study that in Apr 4, 2022 · Abstract. 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. 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. Classifiers using Feature Activations can be Competitive with Raw Activations The probing classifier (also named Auxiliary Prediction Task and Diagnostic Classifier) is a rule- based post hoc explanation method from NLP (Giulianelli et al. As illustrated in figure 2, the process in-volves two probing classifiers: The first Abstract Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. This tutorial showcases how to use linear classifiers to interpret the representation encoded in different layers of a deep neural network. They do this by testing the model's ability to identify certain aspects of language, like relationships between words in a sentence. 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 working process of the probing classifier in this paper is shown in Figure 2. Probing by linear classifiers. 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 or adversarial methods will fail to remove the concept correctly. Probing - Free download as PDF File (. Figure 1: Illustration of the proposed approach for named entity recognition using probing classifiers. , 2020; Belinkov, 2022), if the representation features causally derived from the concept are not predictive enough, the probing classifier for the Motivated by the poor performance of probing classifiers on out-of-distribution (OOD) data, this study hypothesizes that they learn superficial patterns instead of semantic harmfulness. Learn about the construction, utilization, and insights gained from linear probes, alongside their limitations and challenges. However, recent studies have Nov 21, 2025 · Abstract The probing classifiers framework has been employed for interpreting deep neural network models for a variety of natural language processing (NLP) applications. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. Linköping University, Sweden, 2024 Probing Classifiers: Promises, Shortcomings, and Advances. Mar 12, 2025 · To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Streaming text generation, has become a common way of increasing the responsiveness of language model powered applications such as chat assistants. However, recent studies have demonstrated Oct 5, 2016 · Neural network models have a reputation for being black boxes. We demonstrate how this 5 days ago · Embedded Named Entity Recognition using Probing Classifiers. Tools such as deep neural networks regularly outperform humans with such large and high-dimensional datasets The reason is the methods’ reliance on a probing classifier as a proxy for the attribute. The basic idea is simple — a classifier is trained to pre… 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 In this work, we develop an approach we call Em-bedded Named Entity Recognition (EMBER) for performing named entity recognition (NER), a cen-tral IE subtask consisting of mention detection and entity typing, using only a LM’s internal represen-tations as feature space without further finetuning thereof. Oct 1, 2021 · Many scientific fields now use machine-learning tools to assist with complex classification tasks. 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. , 2018; Hupkes et al. Aug 14, 2024 · An MIT team used probing classifiers to investigate if language models trained only on next-token prediction can capture the underlying meaning of programming languages. Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. An early usage of probing tasks can be found in Shi et. 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. Currently, this requires either separate models during inference, which 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. 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 In this work, we develop an approach we call Em-bedded Named Entity Recognition (EMBER) for performing named entity recognition (NER), a cen-tral IE subtask consisting of mention detection and entity typing, using only a LM’s internal represen-tations as feature space without further finetuning thereof. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17830–17850, Miami, Florida, USA. Jenny Kunz (2024) Understanding Large Language Models: Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation Jenny Kunz, Oskar Holmström (2024) The Impact of Language Adapters in Cross-Lingual Transfer for NLU Proceedings of the 1st Workshop on Modular and Open Multilingual NLP (MOOMIN 2024), p. We also include a suite of 10 probing tasks which evaluate what 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. The probe classifier with a suffix and the probe classifier using the exponential-moving-average perform at similar levels to each other, performing between the strength of the S and M models. In neuroscience, automatic classifiers may be usefu… 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. The basic idea is simple — a Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Studies, however, have largely focused on sentencelevel NLP tasks. Jenny Kunz: Understanding Large Language Models: Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation. As illustrated in figure 2, the process in-volves two probing classifiers: The first 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. This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. Yonatan Belinkov Computational Linguistics 2022 [Abstract] [PDF] [Arxiv] 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 Streaming text generation, has become a common way of increasing the responsiveness of language model powered applications such as chat assistants. Probing classifiers are shown in red, with circles symbolizing where representations are accessed. The working process of the probing classi-fier in this paper is shown in Figure 2. Even the developers of frontier AI models have very little idea how they work. However Jul 8, 2022 · The reason is the methods' reliance on a probing classifier as a proxy for the concept. Feb 16, 2024 · Instead, we propose directly embedding information extraction capabilities into pre-trained language models using probing classifiers, enabling efficient simultaneous text generation and information extraction. Abstract: Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Yanai Elazar is postdoctoral researcher on the AllenNLP team at AI2. 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. , 2019; Sagawa et al. Traditional probing methods like logistic regression often face accuracy limitations, making it This paper explores the use of gradient boosting decision trees on the hidden layers of transformer neural networks for probing classifiers. 24 Oct 9, 2024 · Chip-tuning adopts probing classifiers to extract relevant features from intermediate layers of language models, and safely removes subsequent layers without affecting the selected classifier. However, recent studies have demonstrated View recent discussion. Both predic-tions are aggregated into span-level entity predictions. Instance-level examinations, such as attribution techniques, are commonly employed to interpret the model decisions. Gain familiarity with the PyTorch and HuggingFace libraries, for using and evaluating language models. The study aims to improve the process of understanding and interpreting the capabilities of large language models (LLMs) in capturing syntactic features. 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 Probing classifiers are shown in red, with circles symbolizing where representations are accessed. Motivated by the poor performance of probing classifiers on out-of-distribution (OOD) data, this study hypothesizes that they learn superficial patterns instead of semantic harmfulness. By treating the language model as the ‘brain’ and its representations as ‘neural activations’, we decode grammaticality labels of minimal pairs from the Squib Probing Classifiers: Promises, Shortcomings, and Advances Yonatan Belinkov 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. Similar to a neural electrode array, probing classifiers help both discern and edit 5 days ago · Furthermore we propose a probing classifier based solution using VLMs. txt) or read online for free. These properties can include linguistic, visual, or multimodal features. We assess their generalization power by using them as features on a broad and diverse set of "transfer" tasks. 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. 5 days ago · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Nov 28, 2022 · 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 or adversarial methods will fail to remove the concept correctly. Traditional probing methods like logistic regression often face accuracy limitations, making it Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Analyzing the attribu tions across each class within one instance can 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. 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. Apr 11, 2021 · What’s Wrong with Standard Probing? Probing is one of the popular analysis methods, often used for investigating the encoded knowledge in language models. May 14, 2025 · Probing classifiers are one tool that researchers can use to try and achieve this. 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. Even under the most favorable conditions when an attribute’s features in representation space can alone provide 100% accuracy for learning the probing classifier, we prove that post-hoc or adversarial methods will fail to remove the attribute correctly. Both predictions are aggregated into span-level entity predictions. They found that it forms a representation of program semantics to generate correct instructions. One classifier performs token-level entity typing using hid-den states at a single layer, while a second classifier detects spans based on attention weights. One classifier performs token-level entity typing using hidden states at a single layer, while second classifier detects spans based on attention weights. , 2018; Veldhoen et al. The reason is the methods’ reliance on a probing classifier as a proxy for the attribute. from per-token embeddings for tokens within those target spans. However, recent studies have demonstrated SentEval is a library for evaluating the quality of sentence embeddings. 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. 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 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 or adversarial methods will fail to remove the concept correctly. Jul 11, 2025 · Title: Latent Causal Probing: A Formal Perspective on Probing with Causal Models of Data Abstract: As language models (LMs) deliver increasing performance on a range of NLP tasks, probing classifiers have become an indispensable technique in the effort to better understand their inner workings. Poster Probing Classifiers are Unreliable for Concept Removal and Detection Abhinav Kumar · Chenhao Tan · Amit Sharma Keywords: [ Probing ] [ Null-Space Removal ] [ Adversarial Removal ] [ spurious correlation ] [ Fairness ] Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. We propose chip-tuning, a simple and effective structured pruning framework specialized for classification problems. We’ve explained what probing classifiers are and why they could be useful for AI safety. However, recent studies have demonstrated 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. 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. At the same time, extracting semantic information from generated text is a useful tool for applications such as automated fact checking or retrieval augmented generation. This hypothesis is validated by experiments demonstrating the classifiers’ reliance on surface-level features and trigger words. Dr. We designed eight embedding 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 (. One classifier performs token-level entity typing using hidden states at a single layer, while a second classifier detects spans based on attention weights. , 2020c; Arjovsky et al. al (2016) Does String-Based Neural MT Learn Source Syntax? 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. As previous work has argued (Tsipras et al. This article critically reviews the probing classifiers framework, highlighting their promises, shortcomings, and advances. 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. In neuroscience, automatic classifiers may be usefu… Jun 23, 2020 · Many scientific fields now use machine-learning tools to assist with complex classification tasks. 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. However, recent studies have demonstrated Probing classifiers are shown in red, with circles symbolizing where representations are accessed. A typical setup involves (1) defining an auxiliary task consisting of a dataset of text annotated 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. 4 days ago · 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. Mar 18, 2024 · Streaming text generation has become a common way of increasing the responsiveness of language model powered applications, such as chat assistants. However, recent studies have demonstrated Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. However, when interpreting misclassified decisions, human intervention may be required. This is typically carried out by training a set of diagnostic classifiers that predict a specific linguistic property based on the representations obtained from different layers. However, recent studies have demonstrated 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. Sep 18, 2024 · However, probing classifiers offer a technique to evaluate the internal representations of pre-trained models and determine if these representations are informative for downstream tasks. This helps us better understand the roles and dynamics of the intermediate layers. Oct 9, 2024 · In this paper, we adopt the probing technique to explain the layer redundancy in LLMs and demonstrate that language models can be effectively pruned with probing classifiers. Currently, this requires either separate models during inference, which Instead, we propose directly embedding information extraction capabilities into pre-trained language models using probing classifiers, enabling efficient simultaneous text generation and information extraction. Yanai Elazar works on Figure 1: Illustration of the proposed approach for named entity recognition using probing classifiers. Feb 24, 2021 · However, recent studies have demonstrated various methodological limitations of this approach. The basic idea is simple —a 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. Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. This work is the first to apply the probing paradigm to representations learned for document-level information extraction (IE). 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 . These classifiers aim to understand how a model processes and encodes different aspects of input data, such as syntax, semantics, and other linguistic features. However, recent studies have Attention weights: Probe classifiers are built on top of attention weights to discover if there is an underlying linguistic phenomenon in attention weights patterns. Moreover, these probes cannot affect the training phase of a model, and they are generally added after training. The basic idea is simple -- a How Probing Classifiers Work To create a probing classifier, researchers take a pre-trained language model and use it to analyze specific language features or structures. Abstract Inspired by cognitive neuroscience studies, we introduce a novel ‘decoding probing’ method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. Kenneth Li is working on LLM dialogues and interpretability for alignment of LLMs. May 31, 2025 · In this paper we present a model agnostic explainability pipeline for GNNs employing diagnostic 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. However, recent studies have 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. Currently, this requires either separate models during inference, which 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. 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. SentEval currently includes 17 downstream tasks. Recent works in probing language models demonstrate that Oct 31, 2022 · We theoretically and experimentally demonstrate that even under favorable conditions, probing-based null-space and adversarial removal methods fail to remove the sensitive attribute from latent representation. However 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. This pipeline aims to probe and interpret the learned representations in GNNs across various architectures and datasets, refining our understanding and trust in these models. tjxzpjn wzh hqxnwg umm rej bvkr nnr pemusd ikwuje zxb hfhnn ifezzxa qhpl puxyw mxplfkd