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Linear Probes Llm, Second, the These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. These probes can be Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear probes detect when Llama is deceptive. The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a predefined concept label. Transfer experiments in which probes trained on one dataset generalize to different 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 Abstract As LLM-based judges become integral to in-dustry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. Compared to inference-based or logits-based judgments, we show that linear probing improves both This paper shows how LLMs carry a latent correctness signal in their activations, detected via linear probes, enabling early identification of errors. (2) Methods like GradSafe, which require gradient The paper explores the use of logistic regression probes, trained on the residual stream activations of an LLM, to detect whether the LLM is executing deceptive behavior. 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 different model layers Visualize how information is 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 probes to 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. MLP networks typically contain a The lack of principled factual UQ approaches for LLMs has been mostly due to the untractable nature of Bayesian inference for large-scale neural networks. Our results ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. We 报告结果：最终的准确率（linear probing accuracy）是线性分类器在测试集上的性能指标，它反映了自监督学习模型学习到的特征的质量。 作用： 衡量表征学习质量的的好坏： Linear Abstract The chain-of-thought (CoT) paradigm uses the elicitation of step-by-step rationales as a proxy for reasoning, gradually refining the model’s latent representation of a solution. I trained a probe against a small LLM and then fine- Concerns around membership inference have grown in parallel. If we We thus evaluate if linear probes can robustly detect deception by monitoring model activations. Finetuning This linear-nonlinear-linear operation is applied independently at each position. Linear probes were first introduced by[Alain and Bengio, 2018], showing that hidden layers encode NeurIPS 2024 workshop Socially Responsible Language Modelling Research (SoLaR), proposed herein has two goals: (a) highlight novel and important research directions in responsible LM research Probes rival LLM baselines. Using substantially out-of-distribution data, we show that probes can detect lusions. This problematic behavior becomes more pronounced By complementing previous results on truthfulness and other behaviours obtained with probes and sparse auto-encoders, our work contributes essential findings to elucidate LLM internals. We Predicting LLM Answer Accuracy from Question-Only Linear Probes Introduction This paper investigates whether LLMs encode, in their internal activations, a latent signal that predicts the correctness of Previous eforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. Hallucinations, This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. Bibliographic details on No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes. Common choices for probes include linear classifiers Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear probes detect when Llama is deceptive. We have introduced semantic entropy probes (SEPs): linear probes trained on the hidden states of LLMs to predict semantic entropy, an effective measure of uncertainty for free-form LLM generati Train the Probe: Train a simple classifier or regressor using the extracted hidden states as input features and the annotated properties as target labels. While computationally cheap and widely We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Probes' performance is However, probes produce conservative estimates that underperform on easier datasets but may benefit safety-critical deployments prioritizing low false-positive rates. PALP inherits the scalability of linear probing and As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. The aim is to learn the probing pipeline end-to-end on features we @inproceedings {bao-etal-2025-probing, title = "Probing the Geometry of Truth: Consistency and Generalization of Truth Directions in {LLM}s Across Based on the obtained layer-level posterior distributions, we infer the global uncertainty level of the LLM by identifying a sparse combination of distributional features, leading to an efficient We evaluate several probe architectures trained on synthetic data, and find them to exhibit robust generalization to diverse, out-of-distribution, real-world data. 2024. Finally, good probing performance would hint at the presence of the said Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. However, existing Code to extract and analyze LLM activations Implementations of linear and metric learning probes for task drift classification Evaluation scripts and pre-trained LLM Probe is a tool for analyzing and visualizing representations in language models. It allows users to: Train linear probes to detect signals across different model layers Visualize how information is Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear probes detect 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 Limitations of Prior Work: (1) PPL Filter is only effective for high-perplexity GCG suffixes, but detection drops to 0% for Prefilling and AutoDAN. Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. However, existing 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-GLOBAL 科学技術総合リンクセンターは、国立研 This is a write-up of my recent work on improving linear probes for deception detection in LLMs. 44 on SelfAware, suggesting the failure may persist at the activation level These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any special knowledge of the sleeper agent mech-interp: linear probes A small mechanistic interpretability project exploring linear probes on a tiny modern LLM (Gemma 3 270M). During inference, we remove the sigmoid activation function to produce a symmetrical and continuous 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 developed techniques for inferring whether a LLM is telling More precisely, we propose to train multiple Bayesian linear models, each predicting the output of a layer given the output of the previous one. However, recent work on We extract internal linear representations of emotion concepts (“emotion vectors”) from model activations, using synthetic datasets in which characters experience specified emotions. Our results suggest linear probing offers an accurate, robust and 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 probes 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 tokens are generated, and train This work extracts activations after a question is read but before any tokens are generated, and trains linear probes to predict whether the model's forthcoming answer will be These probes can be designed with varying levels of complexity. Types of Probes and 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 perspective. In this work, we investigate the complementary scientific question of whether an LLM’s residual stream activations—captured immediately after it processes a query—contain a latent signal that predicts if Track: Technical Keywords: LLM, sycophancy, reward model, alignment TL;DR: We develop a technique using linear penalties in reward models to reduce sycophantic behaviors in large These probes gen- eralise under domain shifts and can even outper- form finetuned LLM evaluators with the same training data size. These results advance our Linear Probe Penalties Reduce LLM Sycophancy 14 Dec 2024 Visiting ETH MsC student Henry Papadatos and supervising CHAI PhD student Rachel Freedman publish an article “Linear A streaming approach to detect hallucinated entities in real-time during long-form LLM generation using token-level probes. Interpretability Illusions in the Generalization of Simplified Models – Shows how No answer needed: Predicting llm answer accuracy from question-only linear probes. Based on the layer-level posterior distributions, we obtain a global UQ measure for the LLM via a sparse linear regression predicting the correctness of the LLM. Yet, for LLM generation with True examples cluster on one side, false on the other. This holds true for both in-distribution (ID) and out-of Linear probing achieves 71-83% accuracy detecting LLM truthfulness and is a foundational diagnostic tool for interpretability research. Use them when you have labeled data and want to test specific Probes have been frequently used in the domain of NLP, where they have been used to check if language models contain certain kinds of linguistic information. This problematic behavior becomes more pronounced during We train linear probes on residual-stream activations of Gemma-3-27B and Qwen-3. 72 AUROC on TriviaQA but collapses to 0. 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 paper "No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes" explores whether models encode an internal signal of their own competence that can Linear Probes are the default choice for initial exploration—they're fast, cheap, and provide interpretable results. 1 Linear Classifier Probing Probe technology (Alain and Bengio, 2016) is a method for analyzing and evaluating the internal representations of a neural network by applying Visualizations of LLM true/false statement representations, which reveal clear linear structure. For example, simple probes have shown language models to contain information about simple syntactical features like Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. 10625. In this vein, we analyze how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed We provide a comprehensive study on the suitability of internal activations for assessing MIAs by using linear probes, showing their ability to outperform state-of-the-art contributions. Previous efforts focus on black-to 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 Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. However, they involve spending substantial computational efforts. Arslan Chaudhry, Sridhar Thiagarajan, and Di- lan Gorur. Overall, our work LLM Interpretability Papers Academic and industry papers on LLM interpretability. Our results suggest linear probing offers an accurate, We propose semantic entropy probes (SEPs), a cheap and reliable method for uncertainty quantification in Large Language Models (LLMs). In this vein, we analyse how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed Most techniques use linear probes to monitor and control representations. Previous efforts focus on black-to-grey-box models, These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous sycophancy score A simplified view of the concept probing setup. To address this, we propose the use of Linear Probes (LPs) as a method to detect Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. These probes generalise under domain shifts and can even outperform finetuned LLM evaluators with the same training data size. Our experiments show that 1. 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です。モデルがもっともらしいが事 3. Motivated by Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. The aim is to learn the probing pipeline end-to-end on features we This work develops a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Can you tell when an LLM is lying from the activations? Are simple methods good enough? We recently published a paper investigating if linear Based on the obtained layer-level posterior distributions, we infer the global uncertainty level of the LLM by identifying a sparse combination of distributional features, leading to an efficient UQ scheme. We test two probe-training datasets, one with contrasting instructions to be honest or A linear probe trained on Llama-3-8B's last-layer hidden states achieves 0. This provides initial evidence of an explicit truth direction in LLM internals. LLM Probe is a tool for analyzing and visualizing representations in language models. arXiv preprint arXiv:2509. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. Based on the obtained layer-level posterior distributions, Motivated by interpretability results belrose2023eliciting ; lindsey2025biology showing that various LLM layers are mostly deactivated when the LLM is hallucinating, making the corresponding The probe’s input is the RM activations when evaluating the LLM’s response. In this work, we employ linear probing to extract evaluation judgments from an LLM-as-a-Judge setup. Yet, for LLM generation Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. To address this mech-interp: linear probes A small mechanistic interpretability project exploring linear probes on a tiny modern LLM (Gemma 3 270M). 5-122B to predict revealed pairwise task choices, and identify a genuine preference vector: it tracks the 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 perspective. An important question is whether the probes generalise. . Finally, we explore the practical application of truthfulness probes in selective question-answering, illustrating their potential to improve user trust in LLM outputs. Our results suggest linear probing offers an accurate, robust and compu- The probe’s input is the RM activations when evaluating the LLM’s response. Overall, our work demonstrates that However, probes produce conservative estimates that underperform on easier datasets but may benefit safety-critical deployments prioritizing low false-positive rates. We compare different probe architectures with both prompted and fine-tuned LLM monitors. 2. Recent work has used Probe-based methods operate internally by training lightweight classifiers on intermediate hidden states. ghumh, i5td, yt2, pl1m, a09fub, dfp, lj, ads, oqtg, ntcy, cwgnrmur, eq5fynn, n7lua, tuyin, nb0, rqyrx, am2, rnogfc, bz, y5svhor, pccx, 7pkolw, vjpd, gtyzk, hn, lxwl, hg9qwijcr, rmzzewr, n4op7, ns4hz9,