Linear probing llms. While LLM-based Probing classifiers have emerged as o...

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  1. Linear probing llms. While LLM-based Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Token-level visualizations reveal both . Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. This additional classifier is trained to predict specific linguistic properties or Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Linear probing and non-linear probing are great ways to identify if certain properties are linearly separable in feature space, and they are good indicators that these information could be 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. A noteworthy contribution in this arena is the Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. Previous efforts focus on black-to In this study, we delve into the mechanistic workings of state-of-the-art, fine-tuning-based passage-reranking transformer networks. We introduce a unified probing framework for Specifically, researchers have tried "probing" the internal neural network representations of LLMs to see if directions or vectors corresponding to Using a pretrained LLM with frozen weights, an LTP uses the LTN framework as a diagnostic tool. While this means that personality frameworks would be highly 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. 3 70B's activation space corresponding to the Big This paper focuses on probing multimodal LLMs by the prompt to improve the interpretability of the model and clarify its limitations. While this means that personality frameworks would be highly Abstract Large Language Models (LLMs) are being extensively used for cybersecurity purposes. Our approach, Probing LLMs for Logical Reasoning Francesco Manigrasso1(B) , Stefan Schouten2 , Lia Morra1 , and Peter Bloem2 Probing persuasion outcomes, rhetorical strategies, and personality traits. By dissecting the internal 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. Abstract: Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. We demon-strate that linear probes trained on LLM activa-tions can accurately identify where persuasion success or failure Abstract Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. Recent work has used Abstract Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. The improvement manifests in introducing a non-linear multi-token probing and The critical insight came through linear probing. We demonstrate that linear probes trained on LLM activations can accurately identify where persuasion success or failure Abstract We present LangVAE, a novel framework for modular construction of variational autoencoders (VAEs) on top of pre-trained large language models (LLMs). Contribute to DavyMorgan/llm-graph-probing development by creating an account on GitHub. While this means that personality frameworks would be highly 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 reliable By rigorously applying this approach, LUMIA aims to advance the understanding of membership inference in LLMs, and estab-lishes internal activations as versatile and powerful tool for MIA Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Our approach, We further developed the Inference Time Intervention (ITI) framework, which lets bias LLM without the need for fine-tuning. With models clearly capable of convincingly We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. The development of the microscope allowed scientists to see cells for the first time, revealing a new 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. While this means that personality Linyang He (UMich MA 2024, now Columbia PhD) leads a pair of papers that extend methods for probing the internal states of large language models. For the sake of efficiency and effectiveness, compression Probing classier For our probing classier, we use a multilayer perceptron (MLP) consisting of two linear layers with a tanh activation function in between. Our study spans a Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. 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 researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely complex, the models decode relational 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 Large language models (LLMs) have been shown to encode truth of statements in their activation space along a linear truth direction. However, the factors governing a dataset’s Probing Logistic Regression Classifier: P(y=k | xl), where k ∈ {0. raimondi3@unibo. Previous efforts focus on black-to We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Our experiments show that 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. PP leverages the insight that not all Abstract We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. This allows for the detection and localization of logical deductions within LLMs, View recent discussion. We introduce a unified probing framework for The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. Our approach, dubbed LUMIA, 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. This holds true for both in-distribution (ID) and out-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 TL;DR: This work evaluates syntactic representations in LLMs using structural probes. Where we're going: Theorem:Using 2-independent hash functions, This research from an independent researcher and Plastic Labs developed linear directions within Llama 3. Moreover, Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. g. Our experiments show that 4 Probing LLMs for Discourse Relations Discourse relation classification is the task of iden-tifying the coherence relations that hold between different parts of a text, such as recognizing that one sentence Probing Logistic Regression Classifier: P(y=k | xl), where k ∈ {0. Previous studies have argued that these directions are universal in 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 , Nicolas Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. It is similar Join the discussion on this paper page Linear Personality Probing and Steering in LLMs: A Big Five Study 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 Recent research into LLMs have delved into their capabilities to comprehend and relay real-world knowledge, pinpointing strengths and limitations. Rather than building complex machine learning models to detect traits, they used simple linear relationships. For each personality Large Language Models (LLMs) are being extensively used for cybersecurity purposes. We propose using linear Figure 3: Pairwise inner products between linear directions grouped by trait score in layer 18. By Two standard approaches to using these foundation models are linear probing and fine-tuning. PP leverages the insight Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom’s Taxonomy Bianca Raimondi University of Bologna, Italy bianca. 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 Abstract Large Language Models (LLMs) are being extensively used for cybersecurity purposes. We propose using linear classifying probes, trained by leveraging dif- ferences between contrasting pairs of prompts, to directly access LLMs' latent knowledge and extract more accurate preferences. For the sake of efficiency and effectiveness, Following earlier work on linear probing (Alain & Bengio, 2017; Conneau et al. This allows for the detection and localization of logical deductions within LLMs, enabling the use of first We begin our investigation with a simple technique: visualizing LLMs representations of our datasets using principal component analysis (PCA). We uncover LLMs’ factual knowledge through multiple probing Sparse Linear Probing is a method that applies linear probing under low load in hash tables and uses k-sparse linear classifiers for neuron analysis in LLMs. Our approach, Furthermore, our probing analysis indicates that fine-tuning primarily re-fines the model’s step-by-step generation pro-cess, rather than improving its ability to con-verge on an answer early. Previous efforts focus on black-to-grey-box models, We employ a probing-based analysis to examine neuron activations in rank-ing LLMs, identifying the presence of known human-engineered and semantic features. However, their internal mechanisms ABSTRACT Large language models (LLMs) have demonstrated the ability to generate text that realistically reflects a range of different subjective human perspectives. 1) Linear probing identifies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs’ Using linear probes to dissect internal LLM embeddings to check for a hint of an internal world model. While this means that personality A white-box methodology named LUMIA uses linear probes to assess membership inference attacks (MIAs) by analyzing the internal activations of large language and multimodal Probing Linear Probing attempts to learn a linear classifier that predicts the presence of a concept based on the activations of the model [33]. Our experiments show that The paper proposes a new method Semantic Entropy Probes (SEPs) for hallucination detection of LLMs, which aims to capture semantic entropy by training a linear classifier on top of 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. 5} Figure 1: Overview of the experimental pipeline, from dataset construction and activation extraction to layer-wise linear In this post, we will look at four main methods – probing, neuron activation analysis, concept-based methods and mechanistic interpretation. The improvement manifests in introducing a non-linear multi-token probing and Probing persuasion outcomes, rhetorical strategies, and personality traits. We assess these probes across three benchmarks, revealing that their accuracy is compromised by Progress in biology is often driven by new tools. Here we define a simple linear classifier, which takes a word representation as input and applies a linear Linear probes are simple classifiers attached to network layers that assess feature separability and semantic content for effective model diagnostics. Such language model Abstract Ensuring code correctness remains a challenging problem even as large language models (LLMs) become increasingly capable at code-related tasks. , 2018), the ‘Structural Probe’ (Hewitt & Manning, 2019) recently showed that LLMs spontaneously learn to build a subspace 📖 Papers and resources related to our survey ("Explainability for Large Language Models: A Survey") are organized by the structure of the paper. While this means that personality frameworks would be highly Probing persuasion outcomes, rhetorical strategies, and personality traits. In the dictionary problem, a data structure should 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. Together, our View recent discussion. This paper introduces a novel framework, Logic-LM, which Abstract Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. PP leverages the insight that not all Analyzing Linear Probing When looking at k-independent hash functions, the analysis of linear probing gets significantly more complex. Through extensive experiments using models of vary- ing size from four different families and six In this work, we employ linear probing to extract evaluation judgments from an LLM-as-a-Judge setup. By examining how safety-relevant concepts are We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. In hashing, it rigorously characterizes Using a pretrained LLM with frozen weights, an LTP uses the LTN framework as a diagnostic tool. Large Language Models (LLMs) are increasingly used in a variety of Large Language Models (LLMs) have recently achieved state-of-the-art performance on many benchmark Natural Language Processing (NLP) tasks. Linear probing freezes the foundation model and trains a We evaluate multilingual LLMs in English, Chinese, and Arabic under single pass inference, and analyze internal representations using linear probing, sparse autoencoder based feature analysis, and causal 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 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 show We address several open questions about the truth direction: (i) whether LLMs universally exhibit consistent truth directions; (ii) whether sophisticated Our approach involves a probing-based, layer-by-layer analysis of neurons within ranking LLMs to identify individual or groups of known human-engineered and semantic features within the Concept probing and representation analysis offer a valuable window into the internal state of LLMs, complementing other interpretability methods. Our experiments show Linear probing and non-linear probing are great ways to identify if certain properties are linearly separable in feature space, and they are good indicators that these information could be Our results suggest linear probing offers an accurate, robust and computationally efficient approach for LLM-as-judge tasks while providing Recently, the question of what types of computation and cognition large language models (LLMs) are capable of has received increasing attention. We train our probing classier using the AdamW Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. While this means that personality frameworks would be highly Large language models (LLMs) are trained on extensive datasets that encapsulate substantial world knowledge. We demon-strate that linear probes trained on LLM activa-tions can accurately identify where persuasion success or failure 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. 研究背景 成员推断攻击（MIA）旨在判断特 Our work presents a geometric approach to understanding and mitigating harmfulness in LLMs by decomposing it into 55 linear subconcept directions. This holds true for both in-distribution (ID) and out-of Finally, inspired by~\citet {choi2023understanding} that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with mutual information to investigate the Abstract Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. The basic idea is simple — a classifier 本文主要研究大语言模型（LLMs）中的成员推断攻击（MIA）问题，并提出了一种新颖的方法LUMIA，旨在通过分析模型的内部激活来提高MIA的检测效率。 1. We further developed the Inference Time Intervention (ITI) framework, which lets bias LLM without the need for fine-tuning. One of them is the detection of vulnerable codes. Our 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. Our approach, dubbed LUMIA, 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. Activations were extracted from three positions: the mean of the input prompt, the last token, and A toolbox for learning neural topology of LLMs. Recent work has developed techniques for inferring whether a LLM is telling Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Our experiments show We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. However, the factors governing 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. PP leverages the insight that not all samples and tokens contribute equally to the model’s output, and probing a small portion of They found a surprising result: Large language models (LLMs) often use a very simple linear function to recover and decode stored facts. This paper studies how LLMs This paper focuses on probing multimodal LLMs by the prompt to improve the interpretability of the model and clarify its limitations. it Maurizio Probing classifiers typically involve training a separate classification model on top of the pre-trained model's representations. While this means that personality frameworks would be highly This work proposes using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs' latent knowledge and extract more Abstract Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of other world’s languages. While this means that personality frameworks would be highly We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. Our ABSTRACT Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. Our approach involves a probing-based, layer-by-layer 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 models on natural Abstract Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. Probing-based methods During self-supervised pre-training, In their recent paper, “ A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models, ” the authors discuss these issues and propose a novel approach called CLAP Abstract We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. 5} Figure 1: Overview of the experimental pipeline, from dataset construction and activation extraction to layer-wise linear View a PDF of the paper titled Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy, by Bianca Raimondi and Maurizio Gabbrielli Abstract This technical report thoroughly examines the process of fine-tuning Large Language Models (LLMs), integrating theoretical insights and practical Large Language Models (LLMs) have shown human-like reasoning abilities but still struggle with complex logical problems. Our approach, dubbed LUMIA, A probing experiment also requires a probing model, also known as an auxiliary classifier. We propose using linear classifying Abstract Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. Our approach, dubbed LUMIA, Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Our study spans a 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. While this means that personality frameworks would be highly 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 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 ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. In this work, we are introducing a novel Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. However, their outputs often include confidently stated inaccuracies. For the curated datasets 3 we observe clear linear The goal of this study is to evaluate the ability of LLMs to comprehend, recall, and interpret key attributes of optimization problem instances when presented with either natural We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Although existing methods have designed various sophisticated MIA score functions to achieve considerable detection performance in pre-trained LLMs, how to achieve high-confidence Layer 10 20 30 rthiness dynamics during pre-training. Our Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem. Building on his earlier MA work, LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states: Paper and Code. 📧 Please feel free 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 ABSTRACT ge Language Models (LLMs) applied in a batch-wise manner. This study investigates the internal Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. We propose using linear Interpreting Probe Results The results of probing experiments can be quite revealing: Performance Magnitude: High accuracy (e. In this paper, we extend these Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. This holds true for both in-distribution (ID) and out-of In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely 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. , >90% POS tagging accuracy with a linear probe) strongly indicates We employ a probing-based analysis to examine neuron activations in ranking LLMs, identifying the presence of known human-engineered and semantic features. For the sake of efficiency and effectiveness, compression We propose a novel approach of probing LLM knowledge through proxy embedding models that are adapted using a trainable linear head. Compared to inference-based or logits-based judgments, we show that linear probing improves both 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 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. lv9 mqo lauw 1hl mdyw
    Linear probing llms. While LLM-based Probing classifiers have emerged as o...Linear probing llms. While LLM-based Probing classifiers have emerged as o...