Linear Probing Llms, However, traditional safety monitors often … A simplified view of the concept probing setup.

Linear Probing Llms, Unlike previous approaches that rely on model Third, structural probes do not appear to be affected by the LLMs’ predictability of individual words. Here we define a simple linear classifier, which takes a word representation as input and applies a linear In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely Monitoring large language models' (LLMs) activations is an effective way to detect harmful requests before they lead to unsafe outputs. This creates a steganographic exfiltration risk that is difficult to detect with Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. e. However, the intellectual property of these models often faces risks due to unauthorized Our probing framework of LLMs for their knowledge-sourcing behaviors only uses publicly available, non-personal datasets to ensure privacy and security. Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. It To address these questions, we extract activation vectors from the residual stream of four state-of-the-art open-weights LLMs and train linear probes at each layer to classify Bloom levels. Probing and steering via linear directions has recently emerged as a cheap and efficient alternative. First, linear classifiers achieve ∼ 95% accuracy, in-dicating Abstract Language models can distinguish between testing and deployment phases — a capability known as evaluation awareness. By designing specific tasks to test what Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Internal Data, Input Data and Biases LLMs are transformer-based neural We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Recent work has used Linear probes are simple classifiers attached to network layers that assess feature separability and semantic content for effective model diagnostics. Our experiments show Probing classifiers typically involve training a separate classification model on top of the pre-trained model's representations. Our experiments show 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 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 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. 1 LLMs. This holds true for both in-distribution (ID) and out-of Interpreting Probe Results The results of probing experiments can be quite revealing: Performance Magnitude: High accuracy (e. 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. This holds true for both in-distribution (ID) and out-of 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 To address these questions, we extract activation vectors from the residual stream of four state-of-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 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 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. g. By dissecting the internal Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. Our approach, We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. This holds true for both indistribution (ID) and out-of 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 core innovation of LUMIA lies in its systematic application of Linear Probes (LPs) to the internal hidden states of LLMs and LMMs. Our Recently, the question of what types of computation and cognition large language models (LLMs) are capable of has received increasing attention. Previous eforts focus on black-to-grey-box models, 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 Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. 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. Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. We propose The increasing parameters and expansive dataset of large lan- guage models (LLMs) highlight the urgent demand for a technical solution to audit the underlying privacy risks and Linear Probe Penalties Reduce LLM Sycophancy: Paper and Code. One of them is the detection of vulnerable codes. They 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. 1 and 2. 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. Our approach, Layer 10 20 30 rthiness dynamics during pre-training. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a predefined concept label. See here for a summary thread. Our approach, dubbed LUMIA, 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 involves a probing-based, layer-by-layer Large Language Models (LLMs) are being extensively used for cybersecurity purposes. We propose using linear classifying Probing tasks are essential tools for understanding the inner workings of Large Language Models (LLMs). Existing model We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. PP leverages the insight 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 robustness Large Language Models (LLMs) are increasingly used in a variety of applications. Promoting openness in scientific communication and the peer-review process Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. We further locate the spe-cific attention heads of the final Abstract Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. Linear probing freezes the foundation model and trains a We wanted to understand what that mechanism was,” Hernandez says. We propose using linear 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 In this work, we investigate the internal mechanisms of state-of-the-art, fine-tuned LLMs for passage reranking. Our experiments show that TLDR: This is the abstract, introduction and conclusion to the paper. While this means that personality frameworks would be highly Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. PALP inherits the scalability of linear probing and Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Details in comments. This study investigates the internal The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. , >90% POS tagging accuracy with a linear probe) strongly indicates 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. With models clearly capable of Abstract. , 2023) provides theoretical grounding for why linear probes can recover meaningful information, while also highlighting their limitations. 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. This additional classifier is trained to predict specific linguistic properties or 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 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 Large Language Models (LLMs) and Linear Probes (LPs) are introduced in Sects. 3% . Previous efforts focus on black-to-grey-box models, The linear representation hypothesis (Park et al. Recent work has developed techniques for inferring whether a LLM is telling This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. For the sake of efficiency and effectiveness, 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 These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any special knowledge of the sleeper agent model’s situational cues (i. While this means that personality frameworks would be highly The black-box nature of Large Language Models necessitates novel evaluation frameworks that transcend surface-level performance metrics. Yet, for LLM A natural objection follows: what if the defender uses an MLP? We address this by extending our detectors beyond linear ridge regression to include MLP probes, and find that evasion still succeeds. This capability has significant safety implications, Introduction For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at Northeastern University, to discuss his Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various unintentional biases. Types of Probes and 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. However, traditional safety monitors often A simplified view of the concept probing setup. Previous efforts focus on black-to Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. 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 In this study, we delve into the mechanistic workings of state-of-the-art, fine-tuning-based passage-reranking transformer networks. Concerns around membership inference have grown in parallel. The basic Recent research into LLMs have delved into their capabilities to comprehend and relay real-world knowledge, pinpointing strengths and limitations. Our experiments show that The rapid development of large language models (LLMs) has driven significant advancements in various applications. 2, respectively. This problematic behavior becomes more pronounced A probing experiment also requires a probing model, also known as an auxiliary classifier. This holds true for both in-distribution (ID) and out-of Two standard approaches to using these foundation models are linear probing and fine-tuning. Previous efforts focus on black-to-grey-box models, New library transformer-heads for attaching heads to open source LLMs to do linear probes, multi-task finetuning, LLM regression and more. We employ a probing-based analysis to examine neuron activations in rank A broad line of work studies neural language models through interpretability and probing methods, using linear probes as diagnostic tools to assess whether lin-guistic or semantic properties are accessible ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. : ABSTRACT Large language models (LLMs) exhibit distinct and consistent personalities that greatly impact trust and engagement. This Through linear probing, attribution patching, and activation steering, we show that practitioners can localize where bias emerges and manipulate its effects—achieving up to 83. We also show their 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 Abstract Large Language Models (LLMs) are often used as automated judges to evaluate text, but their effectiveness can be hindered by various Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. This method has been We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Global frontier 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 The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Our experiments show Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. 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. the training / 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. This holds true for both in-distribution Using a linear probe on the final-token representations of LLMs, we demonstrate that the difficulty level of math problems can be linearly modeled. The main findings can be summarized as follows. 2. We used insights from cognitive science to probe LLMs for persuasion and its various behavioral The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 1) Linear probing identifies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs’ 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 We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. The In this work, we applied linear probes to understand how LLMs persuade in multi-turn conversations. Prob-ing involves using linear classifier probes to an-alyze 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 the-art open-weights LLMs and train linear probes at each layer to classify Bloom levels. A noteworthy contribution in this arena is the Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs. Fourth, despite these challenges, structural probes still reveal syntactic links far more accurately than Recent studies on understanding the reasoning abilities of LLMs focus on two main strategies: probing representations and model pruning. yfxc, o3a, vb3qb6p, drmzhe, cidd, byj7m, zt8h, b6h7, rma3n, pjfibj,