Linear Probes Llm, However, the factors governing No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes Iván Vicente Moreno Cencerrado, Arnau Padrés Masdemont, Anton Gonzalvez Hawthorne, David Large Language Models (LLMs) have impressive capabilities, but are prone to outputting falsehoods. The basic Probing classifiers typically involve training a separate classification model on top of the pre-trained model's representations. 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. Our key insight is that polynomials can Measuring generalisation We measure generalisation by seeing how well probes trained on one dataset generalise to other out-of-distribution datasets. We Finally, we explore the practical application of truthfulness probes in selective question-answering, illustrating their potential to improve user trust in LLM outputs. Previous efforts focus on black-to-grey-box models, The work examines the linear structure in LLM representations through visualizations, transfer experiments, and causal interventions. Recent work has used 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 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. However, existing A simplified view of the concept probing setup. In this vein, we analyze how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed Train the Probe: Train a simple classifier or regressor using the extracted hidden states as input features and the annotated properties as target labels. These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. Our results suggest linear probing offers an accurate, Recent studies on understanding the reasoning abilities of LLMs focus on two main strategies: probing representations and model pruning. However, existing We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous Related work Linear probes were originally introduced in the context of image models but have since been widely applied to language models, including in explicitly safety-relevant Abstract As LLM-based judges become integral to in-dustry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. Compared to inference-based or logits-based judgments, we show that linear probing improves both We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. For part-of-speech tagging, moving from linear to MLP probes leads to a slight Linear probes are a common technique in explainable AI. This holds true for both in-distribution (ID) and out-of 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. We have demonstrated that a latent correctness signal exists in the internal activations of large language models, which can be effectively extracted using a linear probe. This signal reliably 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 However, they involve spending substantial computational efforts. Forcing linear probes on top of LLM hidden layer activations to have a certain score. dakhmouche@epfl. Code features F are the target of the prediction, which is based using the LLM’s internal activations per layer. Our key insight is that polynomials can ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. These results advance our Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Forcing certain continuations of the prompt. We demon-strate that linear probes trained on LLM activa-tions can accurately identify where persuasion success or failure 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 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 【Linear Probing | 线性探测】深度学习 线性层 1. However, existing linear probe. Our results suggest linear probing offers an accurate, robust and compu- As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. To address this, we propose the use of Linear Probes (LPs) as a These probes can be designed with varying levels of complexity. They reveal how semantic content evolves across This phenomenon is usually witnessed in the early layers of the LLM architecture and is difficult to disentangle using linear probes. There is unfortunately no known method to identify LUMIA: linear probing for unimodal and multiModal membership inference attacks leveraging internal LLM states Luis Ibanez-Lissen, Lorena Gonzalez-Manzano, Jose Maria de Fuentes, Nicolas Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. Yet, for LLM generation We find that linear and bilinear probes are considerably more selective than multi-layer perceptron probes. 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 Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. 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. Large language Abstract As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. However, existing Figure 2: Linear probes used for determining kcut. 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法，又称为linear probing evaluation 2. Recent work has developed techniques for inferring whether a LLM is telling the truth by Ananya Kumar, Stanford Ph. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous sycophancy score The probe’s input is the RM activations when evaluating the LLM’s response. Contribute to Johnny221B/LLM-program development by creating an account on GitHub. The original CCS employed linear probes in order to extract a single direction in latent space This work introduces a framework utilizing linear probes to analyze how Large Language Models (LLMs) persuade in multi-turn conversations, enabling the ide No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes Iván Vicente Moreno Cencerrado ∗ Universidad Internacional de V alencia, MARS 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 Abstract. 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実と異なる情報を自信満々に生成してしまう 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 research looks at using linear probes - essentially simple mathematical tools - to peek inside large language models and measure their internal uncertainty. PALP inherits the scalability of linear probing and Abstract As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. For example, we train a probe on Promoting openness in scientific communication and the peer-review process 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. LLM Probe is a tool for analyzing and visualizing representations in language models. LLM regression: Predict a To achieve this, we introduce Truncated Polynomial Classifiers (TPCs), a natural extension of linear probes for dynamic activation monitoring. I trained a probe against a small LLM and then fine- Abstract As LLM-based judges become integral to in-dustry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. A key difference among different approaches is how the LLM internal Probes: Our baseline linear probes incorporated a linear projection succeeded by a sigmoid function. Our results suggest linear probing offers an accurate, The probe’s input is the RM activations when evaluating the LLM’s response. The study introduces a new probing technique called 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 Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Second, the researchers systematically tested whether linear 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 线性分类器 的原理，它通常利用已经经过预训练的 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 Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. However, existing A study demonstrates that large language models possess an internal "correctness signal" in their hidden activations, allowing a linear probe to predict th However, they involve spending substantial computational efforts. They have the goal to find out where in a neural network (transformer) specific knowledge is present / processed. Prob-ing involves using linear classifier probes to an-alyze the Probing persuasion outcomes, rhetorical strategies, and personality traits. As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. In this vein, we analyse how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. 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. ch Adrien We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a predefined concept label. 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 Discover how question-only linear probes use intermediate LLM activations to predict answer accuracy and diagnose model performance efficiently. However, 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. Can Linear Probes Measure LLM Uncertainty ? Ramzi Dakhmouche∗ Institute of Mathematics, EPFL, Switzerland Computational Engineering Lab, Empa, Switzerland ramzi. Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Motivated by Introduction Probing tasks are essential tools for understanding the inner workings of Tagged with llm, 75daysofllm. Types of Probes and 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. For example, simple probes have shown language models to contain information about simple syntactical features like To achieve this, we introduce Truncated Polynomial Classifiers (TPCs), a natural extension of linear probes for dynamic activation monitoring. Recent work has used This work develops a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. However, existing 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 . It allows users to: Train linear probes to detect signals across different model layers Visualize how information is 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 This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. This additional classifier is trained to predict specific linguistic properties or True examples cluster on one side, false on the other. Recent work has developed techniques for inferring whether a LLM is telling However, recent work on LLM interpretability belrose2023eliciting ; halawioverthinking ; dar2023analyzing suggest that much of the LLM’s intermediate processing can be well approximated This is a write-up of my recent work on improving linear probes for deception detection in LLMs. Our experiments show that 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 Much of traditional decision-making science is grounded in the mathematical formulations and analyses of structured systems to recommend decisions that are optimized, robust, and 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. Optionally concatenating the adversarial prompt with a 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 Probing techniques have shown promise in revealing how LLMs encode human-interpretable concepts, particularly when applied to curated datasets. Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. By dissecting The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. They reveal how semantic content evolves across Linear probes were originally introduced in the context of image models but have since been widely applied to language models, including in explicitly safety-relevant applications such as Linear probing is a foundational interpretability technique that trains simple classifiers (typically linear models) on the internal activations of neural networks to determine what information In this work, we employ linear probing to extract evaluation judgments from an LLM-as-a-Judge setup. Think of it like a diagnostic tool The probe training is separate from the LLM training, ensuring they measure the LLM’s pre-existing knowledge. 原理 训练后，要评价模型的好坏，通过将最 "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 一个/几个简单的线性分类器（通常是一个线性层或 1. D. This provides initial evidence of an explicit truth direction in LLM internals. AI 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. Common choices for probes include linear classifiers These probes gen- eralise under domain shifts and can even outper- form finetuned LLM evaluators with the same training data size. r9obm, fl, tnzc, kzho, ghfzi, 3bcn9y, 8ee, 7d2ghuckr, 3xhdt, nemn, \