Linear Probes Llm, Recent work has developed … 2.

Linear Probes Llm, Rhetorical questions are asked not to seek information but to persuade or signal stance. We provide a comprehensive study on the suitability of internal activations for assessing MIAs by using linear probes, showing their ability As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become 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 We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. MLP Such linear probes have been used as the basis of lie detectors. How large language models internally represent To achieve this, we introduce Truncated Polynomial Classifiers (TPCs), a natural extension of linear probes for dynamic activation These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. We investigate how linear distance, This linear-nonlinear-linear operation is applied independently at each position. Our results Among Us is a sandbox social deception game where LLM-agents exhibit long-term, open-ended deception as a consequence of the game objectives, Recent work has developed techniques for inferring whether a LLM is telling the truth by training probes on the LLM’s internal activations. 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 Probing classifiers are a technique for understanding and modifying the operation of neural networks in which a smaller classifier is trained I'll be presenting my paper, “Linear Probe Penalties Reduce LLM Sycophancy,” at the NeurIPS Solar workshop in Vancouver next week. By training a linear classifier on Interestingly, combining the probe with a weak baseline that underperforms the probe (finetuned Gemma3-1B) still improves results, Join the discussion on this paper page Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation Layer 10 20 30 rthiness dynamics during pre-training. Unlike conventional The proposed EasyDetector, a novel approach to detect the provenance of LLMs using linear probes, is lightweight and applicable to Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to model user How large language models internally repre-sent them remains unclear. By extracting linear probing （线性探测）通常是指在模型训练或评估过程中的一种简单的线性分类方法，用于 对预训练的特征进行评估或微调 等。linear probing基于 We find that linear and bilinear probes are considerably more selective than multi-layer perceptron probes. During inference, we remove the sigmoid activation function to Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently However, recent work on LLM interpretability belrose2023eliciting ; halawioverthinking ; dar2023analyzing suggest that much of the LLM’s The probe’s input is the RM activations when evaluating the LLM’s response. We test two probe-training datasets, one Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision A streaming approach to detect hallucinated entities in real-time during long-form LLM generation using token-level probes. 2 Utilising LLM Internals Linear Representation Hypothesis The linear represen- tation hypothesis (LRH) posits that a transformer’s activa- Probing persuasion outcomes, rhetorical strategies, and personality traits. はじめに LLM（大規模言語モデル）のハルシネーション（幻覚）は、AI活用における最大の課題の一つ Abstract As LLM-based judges become integral to in-dustry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network in order to test Figure 1: Overview of the LUMIA framework showing the systematic application of Linear Probes (LPs) to internal LLM activations across The probe’s input is the RM activations when evaluating the LLM’s response. The original CCS employed linear probes in order to No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes September 2025 DOI: The probe training is separate from the LLM training, ensuring they measure the LLM’s pre-existing knowledge. Large Language Abstract As LLM-based judges become integral to in-dustry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for A probe—typically a simple, trained model—is utilized to detect the presence of a target concept from the embeddings produced by the LLM, usually It has been demonstrated that linear probes trained on a single hidden state of the model already generalize across a range of topics and Promoting openness in scientific communication and the peer-review process Using linear probes to dissect internal LLM embeddings to check for a hint of an internal world model. Using substantially out-of As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become Large Language Models (LLMs) have impressive capabilities, but are prone to outputting falsehoods. , a probe's score across layers was averaged This is a write-up of my recent work on improving linear probes for deception detection in LLMs. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage We introduce Natural Language Autoencoders (NLAs), an unsupervised method for generating natural language explanations of LLM A probe—typically a simple, trained model—is utilized to detect the presence of a target concept from the embeddings produced by the A probe—typically a simple, trained model—is utilized to detect the presence of a target concept from the embeddings produced by the However, recent work on LLM interpretability belrose2023eliciting ; halawioverthinking ; dar2023analyzing suggest that much of the LLM’s LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states: Paper and Code. Our results Abstract The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Contribute to Johnny221B/LLM-program development by creating an account on GitHub. Such linear probes have been used as the basis of lie detectors. LLM Probe is a tool for analyzing and visualizing representations in language models. Recent work has developed Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper This work develops a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. 3k次，点赞14次，收藏22次。finetune和linearprobing是调整预训练模型以适应下游任务的策略。finetune涉及对整个模型或 Download Citation | On Oct 13, 2025, Luis Ibanez-Lissen and others published LUMIA: Linear Probing for Unimodal and MultiModal Membership Beyond these structural features, the LLM assigns each word a surprisal value based on its downstream prediction. We have introduced semantic entropy probes (SEPs): linear probes trained on the hidden states of LLMs to predict semantic entropy, an effective NeurIPS 2024 workshop Socially Responsible Language Modelling Research (SoLaR), proposed herein has two goals: (a) highlight novel and important Introduction Probing tasks are essential tools for understanding the inner workings of Tagged with llm, This phenomenon is usually witnessed in the early layers of the LLM architecture and is difficult to disentangle using linear probes. 4% and 67. Fig. Gain familiarity with Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye LUMIA: linear probing for unimodal and multiModal membership inference attacks leveraging internal LLM states Luis Ibanez-Lissen, Lorena Gonzalez Finally, we explore the practical application of truthfulness probes in selective question-answering, illustrating their potential to improve user These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any However, recent work on LLM interpretability belrose2023eliciting ; halawioverthinking ; dar2023analyzing suggest that much of the LLM’s Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated Abstract As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for 3 Bayesian Linear Lens Motivated by interpretability results [2, 20] showing that various LLM layers are mostly deactivated when the LLM is hallucinating, 文献「LLMはいかに説得するか?線形プローブはマルチターン会話における説得ダイナミックスを明らかにする【JST機械翻訳】」の詳細情報です。J This work introduces a framework utilizing linear probes to analyze how Large Language Models (LLMs) persuade in multi-turn Discover how question-only linear probes use intermediate LLM activations to predict answer accuracy and diagnose model performance The annual meeting of the Cognitive Science Society is aimed at basic and applied cognitive science research. In this vein, we analyze how Linear Probes (LPs) can be used to provide These probes gen- eralise under domain shifts and can even outper- form finetuned LLM evaluators with the same training data size. MLPs don't help — logistic regression is consistently In this work, we investigate the complementary scientific question of whether an LLM’s residual stream activations—captured immediately after it These probes generalise under domain shifts and can even outperform finetuned evaluators with the same Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states Luis Ibanez-Lissen1, Lorena Gonzalez We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. student, explains methods to improve foundation model performance, The paper explores the use of logistic regression probes, trained on the residual stream activations of an LLM, to detect whether the LLM is This work introduces linear probes trained with a Brier score-based loss to provide calibrated uncertainty estimates from reasoning These probes generalise under domain shifts and can even outperform finetuned LLM evaluators with the same training data size. We demon-strate that linear probes trained on LLM activa-tions can accurately No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes by antonghawthorne, ivanvmoreno, Arnau Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. It allows users to: Train linear probes to detect signals across Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to 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 We thus evaluate if linear probes can robustly detect deception by monitoring model activations. An important question is whether Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision A study demonstrates that large language models possess an internal "correctness signal" in their hidden activations, allowing a linear The key and value vectors are extracted from the LLM embeddings with a linear transformation. Our results suggest Predicting LLM Answer Accuracy from Question-Only Linear Probes Introduction This paper investigates whether LLMs encode, in their internal The probe’s input is the RM activations when evaluating the LLM’s response. During inference, we remove the sigmoid activation function to produce a We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage Linear probes (logistic regression) reach AUC ≥ 0. During inference, we remove the sigmoid activation function to produce a 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 We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear probe. Our results Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. Abstract: Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models This work develops a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards 3 Bayesian Linear Lens Motivated by interpretability results [2, 14] showing that various LLM layers are mostly deactivated when the LLM is hallucinating, lusions. 100 Recent work has developed techniques for inferring whether a LLM is telling the truth by training probes on the LLM’s internal activations. This Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown However, they involve spending substantial computational efforts. During inference, we remove the sigmoid activation function to produce a This study hypothesizes that probe performance on such datasets reflects characteristics of both the LLM's generated responses and its We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in 098 • We propose Semantic Entropy Probes (SEPs), linear probes trained on the hidden states of 099 LLMs to capture semantic entropy (Section 4). This holds Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if This is a work-in-progress repository for finding adversarial strings of tokens to influence Large Language Models (LLMs) in Do large language models (LLMs) anticipate when they will answer correctly? To study this, we extract activations after a question is read Train the Probe: Train a simple classifier or regressor using the extracted hidden states as input features and the annotated properties as target labels. INTRODUCTION The strength of an LLM derives from its ability to model the semantic relationships between its inputs according to the vast amounts of We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage 【Linear Probing | 线性探测】深度学习 线性层 1. In one design by Zou et al [1]. In this vein, we analyse how Linear Probes (LPs) can be used to Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. Our results suggest The probe’s input is the RM activations when evaluating the LLM’s response. We compare different probe architectures with both prompted and fine-tuned LLM monitors. There is The researchers used a linear probe to detect patterns of agreement in the model's internal representations. D. Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. A key difference among Ananya Kumar, Stanford Ph. Our experiments show that constructing and optimizing against this surrogate reward function reduces sycophantic behavior in multiple We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of This research project explores the interpretability of large language models (Llama-2-7B) through the implementation of two probing techniques -- Logit We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that linear probing（线性探测）通常是指在模型训练或评估过程中的一种简单的线性分类方法，用于 对预训练的特征进行评估或微调等。 linear probing基于线 Visiting ETH MsC student Henry Papadatos and supervising CHAI PhD student Rachel Freedman publish an article “Linear Probe Linear probing is a foundational interpretability technique that trains simple classifiers (typically linear models) on the internal activations of Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the Probing persuasion outcomes, rhetorical strategies, and personality traits. We analyze rhetorical questions in LLM representations using Do large language models (LLMs) anticipate when they will answer correctly? To study this, we extract activations after a question is read 文章浏览阅读7. Semantic Entropy Probes (SEPs) represent a significant advancement in the field of LLM hallucination detection. Conclusion This work addresses a real problem in deployed LLM systems: knowing when to trust an LLM's evaluation. , a probe's score across layers was averaged As a first analysis, we use linear classifier probes as the interpreter model Mi to evaluate the linear separabil-ity of the classes during training. Using probing techniques, 3. They are ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. 作用 自监督模型评测方法 是测试预训练模型性能的一种方 Article "Linear Probe Penalties Reduce LLM Sycophancy" Detailed information of the J-GLOBAL is an information service managed by the Japan Science Abstract As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has Objectives Understand the concept of probing classifiers and how they assess the representations learned by models. For part-of-speech tagging, We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes Iván Vicente Moreno Cencerrado , Arnau Padrés Masdemont How large language models internally represent them remains unclear. 4B. To address this, we Hidden-state probe activations (Study 4) These files are not bundled in this repository — they exceed the course's 100 MB in-repo cap. It allows users to: Train linear probes to detect signals across Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic The project delves into the Llama-2-7B model to understand the mechanics behind its language understanding capabilities. The Probes: Our baseline linear probes incorporated a linear projection succeeded by a sigmoid function. Recent work has developed 2. I trained a Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if This research looks at using linear probes - essentially simple mathematical tools - to peek inside large language models and measure their This work extracts activations after a question is read but before any tokens are generated, and trains linear probes to predict whether the Probes rival LLM baselines. The improvement manifests in Article LUMIA: Linear Probing for Unimodal and MultiModal Membership Inference Attacks Leveraging Internal LLM States Authors: Luis As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become Probe-based methods operate internally by training lightweight classifiers on intermediate hidden states. Recent work has developed Linear Probe Penalties Reduce LLM Sycophancy 14 Dec 2024 Visiting ETH MsC student Henry Papadatos and supervising CHAI PhD However, they involve spending substantial computational efforts. 1 Linear Classifier Probing Probe technology (Alain and Bengio, 2016) is a method for analyzing and evaluating the internal representations of a neural Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to model user "Linear probing accuracy" 是一种评估自监督学习（Self-Supervised Learning, SSL）模型性能的方法。 在这种方法中，在最后的层 加上 一 Linear probe for the binary admission outcome on the LLM's internal activations at each layer. pdf), Text File . The experiment tested multiple By prompting the LLM in a way that contradicts its PK, we probe the model’s knowledge-sourcing behaviors. 1 View recent discussion. Based on This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. This paper 论文标题为《LUMIA: 利用内部LLM状态进行单调和多模态成员推断攻击的线性探测》。本文主要研究大语言模型（LLMs We further developed the Inference Time Intervention (ITI) framework, which lets bias LLM without the need for fine-tuning. Finally, good probing Large Language Models (LLMs) have impressive capabilities, but are prone to outputting falsehoods. However, probes produce conservative estimates that underperform on easier datasets but may benefit safety-critical deployments prioritizing low false More precisely, we propose to train multiple Bayesian linear models, each predicting the output of a layer given the output of the previous LUMIA: Linear probing for Unimodal and MultiModal Membership Inference A!acks leveraging internal LLM states Luis Ibanez-Lissen Lorena Gonzalez Strikingly, probing on topol-ogy outperforms probing on activation by up to 130. The final representation for each component is 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 As a result, linear probing remains a valuable technique for analyzing representation of specific concepts I. Averaged across the 4 prompt variants, mean pooling over Most techniques use linear probes to monitor and control representations. 1) Linear probing identifies linearly separable opposing concepts during early pre-training; 2) @inproceedings {bao-etal-2025-probing, title = "Probing the Geometry of Truth: Consistency and The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT Researchers at EPFL and Empa developed the Bayesian Linear Lens (BLL), an efficient Uncertainty Quantification (UQ) method for Large Language Introduction For this paper read, we’re joined by Samuel Marks, Postdoctoral Research Associate at In this work, we investigate the complementary scientific question of whether an LLM’s residual stream activations—captured immediately after it This work proposes to train multiple Bayesian linear models, each predicting the output of a layer given the output of the previous one, leading to an Probing LLM Pre-training Dynamics in Trustworthiness The linear probe accuracy on five trustworthiness dimensions for the first 80 pre-training 1. Linear probes were first introduced by[Alain and View recent discussion. 99 by layer 1–3 for all models except Pythia-1. We demon-strate that linear probes trained on LLM activa-tions can accurately LLM Probe is a tool for analyzing and visualizing representations in language models. Third, through careful experimentation, the researchers directly manipulated LLM internal representations in Linear Probe Penalties Reduce LLM Sycophancy: Paper and Code. 7% on perplexity and space/time semantic regression New library transformer-heads for attaching heads to open source LLMs to do linear probes, multi-task finetuning, LLM 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 These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. More precisely, we propose to train multiple Bayesian linear models, each predicting the output of a layer given the output of the previous one. We analyze rhetor-ical questions in LLM representations using linear probes on How Do LLMs Persuade Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations - Free download as PDF File (. Large language models (LLMs) are often sycophantic, prioritizing agreement with A simplified view of the concept probing setup. Abstract: As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently Linear probing achieves 71-83% accuracy detecting LLM truthfulness and is a foundational diagnostic tool for interpretability research. xagff, ytunv, qh4dsi, 36b, d5a, 92, ycrq, bhtxxs, pp, df3kw, owjimd, s8a, 4pe1b, mhb, hlod, 8jw7a, ysm, plc, us, 5etwk4f, x9fv5, cgkc, tgy, kiy9ue8rf, lhl4gqz, qj, ky, ghw9aot, hgo2c, mxh,