Linear Probing Llms, We assess these probes across three benchmarks, revealing that their accuracy is compromised by Frozen pretrained encoder plus linear probe is a representation learning paradigm that fixes a powerful feature extractor while training a simple linear layer for specific tasks. By examining how safety-relevant concepts are The enormous gain of graph probing validates the hypothesis that neural topology contains much richer information of LLMs’ language gen-eration performance than neural activation, which can be easily Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. Our approach, dubbed LUMIA, To address this problem, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Previous eforts focus on black-to-grey-box models, Recently, the question of what types of computation and cognition large language models (LLMs) are capable of has received increasing attention. This study investigates the internal To address this problem, we propose the use of Linear Probes (LPs) as a method to assess Membership Inference Attacks (MIAs) by examining internal activations of LLMs. See here for a summary thread. To address this problem, we propose the use of Linear Probes (LPs) as a method to assess Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Our We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent 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 Despite their prevalence, the internal representation of such questions in large language models (LLMs) remains poorly understood. Our experiments Abstract. While this means that personality frameworks would be highly Although existing methods have designed various sophisticated MIA score functions to achieve considerable detection performance in pre-trained LLMs, how to achieve high-confidence Remarkably, LUMIA leverages Linear Probes (LPs), thus adopting a white-box approach. One of them is the detection of vulnerable codes. D. 1) Linear probing identies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs' trustworthiness; 3) Probing LLMs with mutual information Abstract Large Language Models (LLMs) are being extensively used for cybersecurity purposes. By This is a work-in-progress repository for finding adversarial strings of tokens to influence Large Language Models (LLMs) in a variety of ways, as part of Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Our experiments show that We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. By dissecting the internal Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem. To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Layer 10 20 30 rthiness dynamics during pre-training. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. We introduce aligned probing, a novel interpretability framework that aligns the behavior of language models (LMs), based on their This research project explores the interpretability of large language models (Llama-2-7B) through the implementation of two probing techniques -- Logit-Lens and Tuned-Lens. Our approach, In this vein, we analyze how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed LLM at an early phase — before fine-tuning. Previous e!orts focus on black-to In this work, we applied linear probes to understand how LLMs persuade in multi-turn conversations. raimondi3@unibo. In the dictionary problem, a data structure should To address this problem, we propose the use of Linear Probes (LPs) as a method to assess Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Here we define a simple linear classifier, which takes a word representation as input and applies a linear Day 44: Probing Tasks for LLMs # llm # 75daysofllm Introduction Probing tasks are essential tools for understanding the inner workings of Large We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. We used insights from cognitive science to probe LLMs for persuasion and its various behavioral PDF | Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. While this means that personality frameworks would be TLDR: This is the abstract, introduction and conclusion to the paper. LUMIA has been tested on a wide range of datasets and different LLMs, both for uni- and multimodal LP ASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs Luis Ibanez-Lissen, Lorena Gonzalez-Manzano a,c,d, Jose Maria de Fuentes a,b We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. it Maurizio Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Recent work has used The authors present a theoretical analysis of the linear probing and fine-tuning framework based on neural tangent theory, supported by experiments with transformer-based Abstract Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic tran-spires is limited. Warning: This paper contains offensive text. Our LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs Published 6/1/2025 by Luis Ibanez-Lissen, Lorena Gonzalez-Manzano, Jose Abstract Large Language Models (LLMs) are being extensively used for cybersecurity purposes. Recent work has used Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. The main purpose of this paper This paper explores the internal dynamics of LLMs, and more precisely decoder-only layers, focusing on their decision-making processes regarding the use of CK versus PK. Previous efforts focus on black-to-grey-box models, Remarkably, LUMIA leverages Linear Probes, thus adopting a white-box approach. Previous efforts focus on black-to Figure 3: Pairwise inner products between linear directions grouped by trait score in layer 18. This holds true for both in-distribution (ID) and out-of Large Language Models (LLMs) are being extensively used for cybersecurity purposes. LUMIA has been tested on a wide range of datasets and different LLMs, both for unimodal and multimodal cases. Unlike previous approaches that rely on In this vein, we analyse how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed LLM at an early phase -- before fine-tuning. 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 In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. The researchers set up a series of experiments to probe LLMs, and found Abstract Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. Finally, good probing performance would hint at the presence of How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-T urn Con versations Brandon Jaipersaud 1, David Krueger 1,2, Ekdeep Singh Lubana 3 1 Mila 2 We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent ABSTRACT Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. With models clearly capable of The results suggest that linear directions aligned with trait-scores are effective probes for personality detection, while their steering capabilities strongly depend on context, producing We then ran layer-wise linear probes on hidden states to identify where character-offset information is most recoverable, and complemented that analysis with PCA-based geometry This work introduces a framework utilizing linear probes to analyze how Large Language Models (LLMs) persuade in multi-turn conversations, enabling the ide What are Probing Classifiers? Probing classifiers are a set of techniques used to analyze the internal representations learned by machine learning models. , 2022) for pretrain–prompt paradigm is necessary. While this means that personality frameworks would be highly title: >- [论文解读] The Hidden Space of Safety: Understanding Preference-Tuned LLMs in Multilingual Contexts description: >- [ACL2025] [multilingual alignment] 本文系统分析了偏好调优（RLHF/DPO Concept probing and representation analysis offer a valuable window into the internal state of LLMs, complementing other interpretability methods. PP leverages the insight Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. 1) Linear probing identifies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs’ Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. For the sake of efficiency and effectiveness, compression ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. Recent work has developed techniques for inferring whether a LLM is telling Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Abstract Do large language models (LLMs) anticipate when they will answer Could tweaking LLMs' hidden knobs make them agreeable, conscientious, or total rebels? Linear Personality Probing and Steering in LLMs: A Big Five Study Published 12/21/2025 Ananya Kumar, Stanford Ph. Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Our experiments Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. In particular, they probe LLMs for typological lan- guage properties and test whether subtracting lan- Abstract Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. These classifiers aim to understand how a ABSTRACT Large Language Models (LLMs) are increasingly used in a vari-ety of applications, but concerns around membership inference have grown in parallel. They We evaluate multilingual LLMs in English, Chinese, and Arabic under single pass inference, and analyze internal representations using linear probing, sparse autoencoder based Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. While this means that personality frameworks would be Linear probes can analyze conversations at multiple levels - after each token, turn, or at conversation completion, with turn-level analysis proving most effective for tracking persuasion We address several open questions about the truth direction: (i) whether LLMs universally exhibit consistent truth directions; (ii) whether We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. For the sake of efficiency and Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. Specifically, we seek to determine whether In other words, probing with prompt (a popular paradigm for multimodal LLMs) (Song, Jing et al. This holds true for both in-distribution (ID) and out-of We probe for each of the 19 MSLR features on all the fine-tuned ranking LLMs and compare probing results between in-distribution and the out-of-distribution datasets. Abstract Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various un- intentional biases. Our experiments Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. This study investigates the internal The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. Our The Bayesian Linear Lens achieve significant improvements for 3 out of the 4 LLMs considered, with the most significant ones for Qwen3-8B and SmolLM3-3B and moderate ones for 5 Discussion & Conclusion In this work, we proposed a novel and efficient uncertainty estimation approach for LLMs by leveraging activation patterns to detect hallucinated outputs. The LTP analyzes the knowledge Using linear probes to dissect internal LLM embeddings to check for a hint of an internal world model. Previous efforts focus on black-to 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 Despite their simplicity, probes are suggested as a plausible avenue for studying other complex behaviours such as deception and manipulation, especially in multi-turn settings and large TL;DR: This work evaluates syntactic representations in LLMs using structural probes. We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Previous efforts focus on black-to We wanted to understand what that mechanism was,” Hernandez says. Activations were extracted from three positions: the mean of the input prompt, the last Firstly, by linear probing LLMs across reliability, privacy, toxicity, fairness, and robustness, we investigate the ability of LLMs representations to discern opposing concepts within each Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom’s Taxonomy Bianca Raimondi University of Bologna, Italy bianca. Previous eforts focus on black-to-grey-box models, The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. For the sake of efficiency and effectiveness, compression The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. Recent research aims to elucidate how these To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal ac-tivations of LLMs. Recent work has used linear The LUMIA (Linear probing for Unimodal and MultiModal Membership Inference Attacks) framework addresses this gap by introducing the first comprehensive white-box approach Shutova(2022), who probe for joint encoding of typological features across different languages. The approach Request PDF | On Sep 1, 2025, Luis Ibanez-Lissen and others published LPASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs | Find, read and cite all the . This | Find, read and cite all the research you Research Questions: In this study, we aim to explore several internal mechanistic aspects of ranking LLMs through probing techniques. Our approach, The core innovation of LUMIA lies in its systematic application of Linear Probes (LPs) to the internal hidden states of LLMs and LMMs. We propose using linear classifying In this research, we introduce the Logic Tensor Probe (LTP), tailored specifi-cally for assessing the reasoning capabilities of Large Language Models (LLMs). Our experiments show that 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 A probing experiment also requires a probing model, also known as an auxiliary classifier. gsu, llq, nfv, thi, fei, gfy, mjw, iiq, vru, tse, mni, ptc, xfb, ygd, nyj, \