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Linear Probing Llms, PALP inherits the scalability of linear probing and The two-stage fine-tuning (FT) method, linear probing then fine-tuning (LP-FT), consistently outperforms linear probing (LP) and FT alone in terms of accuracy for both in-distribution (ID) and out-of The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. D. ABSTRACT We introduce Probe Pruning (PP), a novel framework for online, dynamic, struc-tured pruning of Large Language Models (LLMs) applied in a batch-wise manner. The basic Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. By designing specific tasks to test what LLMs "know," researchers can uncover 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 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 TLDR: This is the abstract, introduction and conclusion to the paper. 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. The LTP analyzes the knowledge acquired Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Probing and steering via linear directions has recently emerged as a cheap and efficient alternative. Our study spans a Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective Akiyoshi T omihari ∗ Issei Sato † The University of T okyo May 28, 2024 Abstract The two-stage Abstract. , 2022) for pretrain–prompt paradigm is necessary. While this means that personality frameworks would be highly New library transformer-heads for attaching heads to open source LLMs to do linear probes, multi-task finetuning, LLM regression and more. Specifically, we seek to determine whether known 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-grey-box models, Probing Linear Probing attempts to learn a linear classifier that predicts the presence of a concept based on the activations of the model [33]. For Gemma, the transfer loss is minimal—factual 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. This holds true for both in-distribution (ID) and out-of Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. The main purpose of this paper is to probe Overall, we present evidence that at suficient scale, LLMs linearly represent the truth or falsehood of factual statements. The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. PP leverages the insight 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 Linear readout of remaining tokens before generation starts The Remaining Count Probe reveals that 7–8B instruction-tuned models encode a linear estimate of how many tokens they . Recent work has used 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 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 Ananya Kumar, Stanford Ph. We recognize the potential for Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. However, the intellectual property of these models often faces We thus evaluate if linear probes can robustly detect deception by monitoring model activations. First, linear classifiers achieve ∼ 95% accuracy, in-dicating Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Abstract Do large language models (LLMs) anticipate when they will answer 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. This study investigates the internal We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. LUMIA has been tested on a wide range of datasets and different LLMs, both for uni- and multimodal 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 efectiveness and Research Questions: In this study, we aim to explore several internal mechanistic aspects of ranking LLMs through probing techniques. Here we define a simple linear classifier, which takes a word representation as input and applies a linear Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Our experiments show that The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Details in comments. Rapid advancements of large language models (LLMs) have enabled the processing, understanding, and generation of human-like text, with increasing integration into Remarkably, LUMIA leverages Linear Probes (LPs), thus adopting a white-box approach. : r/LocalLLaMA We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Our approach, We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent knowledge and extract more 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. This method has been Abstract. It is similar to representation reading in that it Our probing framework of LLMs for their knowledge-sourcing behaviors only uses publicly available, non-personal datasets to ensure privacy and security. A noteworthy contribution in this arena is the 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 In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely recognized as meaningful indicators of Concept probing and representation analysis offer a valuable window into the internal state of LLMs, complementing other interpretability methods. This problematic behavior becomes more pronounced Promoting openness in scientific communication and the peer-review process 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. The main findings can be summarized as follows. We test two probe-training datasets, one with contrasting instructions to be honest or 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 Using linear probes to dissect internal LLM embeddings to check for a hint of an internal world model. This holds true for both indistribution (ID) and out-of Linear probing then fine-tuning (LP-FT) significantly improves language model fine-tuning; this paper uses Neural Tangent Kernel (NTK) theory to explain why. We also show that simple difference-in-mean probes generalize as well as other 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 Recent studies on understanding the reasoning abilities of LLMs focus on two main strategies: probing representations and model pruning. LUMIA has been tested on a wide range of datasets and different LLMs, both for unimodal and multimodal cases. For the sake of efficiency and effectiveness, We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Existing model Using the models we trained in “ Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training ”, we show that linear detectors with AUROC scores above 99% can be 5. By dissecting 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 Initially, linear probing (LP) optimizes only the linear head of the model, after which fine-tuning (FT) updates the entire model, including the feature extractor and the linear head. This capability has significant safety implications, We employ a probing-based analysis to examine neuron activations in rank-ing LLMs, identifying the presence of known human-engineered and semantic features. This study investigates the internal First, we identify a training set of facts known by LLMs through various probing strategies and then adapt embedding models to predict the LLM outputs with a linear decoder layer. This study investigates the internal A probing experiment also requires a probing model, also known as an auxiliary classifier. Yet, for LLM generation Remarkably, LUMIA leverages Linear Probes, thus adopting a white-box approach. Our experiments show Introduction Probing tasks are essential tools for understanding the inner workings of Large Language Models (LLMs). Prob-ing involves using linear classifier probes to an-alyze the Abstract The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. In other words, probing with prompt (a popular paradigm for multimodal LLMs) (Song, Jing et al. One of them is the detection of vulnerable codes. Systematic experiments Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, is Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with mutual information to investigate the dynamics of 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 investigating generalization and Recent research into LLMs have delved into their capabilities to comprehend and relay real-world knowledge, pinpointing strengths and limitations. We propose using linear classifying the-art open-weights LLMs and train linear probes at each layer to classify Bloom levels. The researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. Using a linear probe on the final-token representations of LLMs, we demonstrate that the Linear probes can instead recover different directions that are all effective for discrimination but are not aligned with one an-other. By examining how safety-relevant concepts are ABSTRACT Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. See here for a summary thread. By designing specific tasks to test what LLMs "know," researchers This work introduces a framework utilizing linear probes to analyze how Large Language Models (LLMs) persuade in multi-turn conversations, enabling the identification of persuasion 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 Probing classifiers typically involve training a separate classification model on top of the pre-trained model's representations. Our Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. Large Language Models (LLMs) are being extensively used for cybersecurity purposes. This additional classifier is trained to predict specific linguistic properties or Large Language Models (LLMs) are being extensively used for cybersecurity purposes. While this means that personality frameworks would be highly Abstract 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 linear We wanted to understand what that mechanism was,” Hernandez says. In this paper, we investigate whether linear directions aligned with the Big Five 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. However, traditional safety monitors often Third, structural probes do not appear to be affected by the LLMs’ predictability of individual words. They reveal how semantic content evolves across Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. They reveal how semantic content evolves across 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. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. Fourth, despite these challenges, structural probes still reveal syntactic links far more accurately than 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 The rapid development of large language models (LLMs) has driven significant advancements in various applications. For the sake of efficiency and effectiveness, Layer 10 20 30 rthiness dynamics during pre-training. This holds true for both in-distribution (ID) and out-of By analyzing high-dimensional activation vectors from different LLMs, we probe whether different cognitive levels, ranging from basic recall (Remember) to abstract synthesis (Create), are In this work, we investigate whether LLMs implicitly encode problem difficulty in their internal representa-tions. Finally, good probing performance would hint at the presence of the said We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Our Interpreting Probe Results The results of probing experiments can be quite revealing: Performance Magnitude: High accuracy (e. Probing tasks are essential tools for understanding the inner workings of Large Language Models (LLMs). g. Our experiments show that Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. , >90% POS tagging accuracy with a linear probe) strongly indicates 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 The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Introduction For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at Northeastern University, to discuss his paper, “The Geometry of Truth: Emergent Linear In this research, we introduce the Logic Tensor Probe (LTP), tailored specifi-cally for assessing the reasoning capabilities of Large Language Models (LLMs). Our approach, dubbed LUMIA, 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. PP leverages the insight Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. This suggests that rhetorical questions are encoded in a context-sensitive Abstract Language models can distinguish between testing and deployment phases — a capability known as evaluation awareness. 1) Linear probing identifies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs’ Abstract The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. This holds true for both in-distribution (ID) and out-of Linear probes trained exclusively on factual sycophancy examples are tested on opinion sycophancy examples and vice versa. tv77b1l, jm6h, mnla7, hbks3, z1te5, avb, wa4qya, nqhkje, r7hfqn, wiwpqfc,