Llm linear probing. We first adapt ex-isting approaches to model calibration .

Llm linear probing This way, you can add new heads to you LLM to finetune it for a completely different task. . Identifying Context Neurons We describe our probing pipeline to identify context neurons - neurons that are sensitive to or encode desired features - in the LLM architecture. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. These mechanisms can be leveraged to see what the model knows about different subjects and possibly to correct false information it has stored. Jan 31, 2025 · In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. , 2019) suggests that a linguistic hierarchy emerges in the LLM layers, with lower layers better suited to solving syntactic tasks and higher layers employed for semantic Oct 24, 2024 · Similarly, the 13B models contains 40 layers and 5120 dimensional vectors. with, we apply linear probing to LLMs. In open addressing solutions to this problem, the data 5 days ago · 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 trustworthiness during pre-training. Taken together, these insights lead to our final Auden-voice encoder, which balances identity and paralinguistic cues while integrating competitively with LLMs toward general-purpose voice encoder. By performing layer-wise probing on the LLM, we can find the distribution of certain ranking related properties. To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining May 17, 2024 · Linear probing is a technique used in hash tables to handle collisions. Middle and high layers representations exhibit relatively high linearly separable patterns about trustworthiness than low layers. We then show that LLMs are very bad at manipulating knowledge they learn during pre-training unless a chain of thought is used at inference time. Probing classifiers typically involve training a separate classification model on top of the pre-trained model's representations. The original CCS employed linear probes in order to extract a single direction in latent space corresponding to latent belief; however, in our work, the relationship between truth, falsehood, and uncertainty/ambiguity may be a complex nonlinear Apr 21, 2015 · Hashing - Part 1: Linear Probing Michael Mroczka 799 subscribers 83K views 9 years ago Mar 22, 2025 · Our results suggest linear probing offers an accurate, robust and computationally efficient approach for LLM-as-judge tasks while providing interpretable insights into how models encode judgement-relevant knowledge. Linear probing accuracy of three LLM families fication problem via a prompting way. We first adapt ex-isting approaches to model calibration 3. Please use this form only to correct data that is out of line with the PDF. 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 of LLM activation probes. Aug 19, 2024 · In this work, we use high-quality datasets of simple true/false statements to study in detail the structure of LLM representations of truth, drawing on three lines of evidence: 1. When holding the LLM weights constant (also known as “linear probing”), the training process can further benefit from Ludwig optimizations like cached encoder embeddings for up to a 50x speedup. Apr 4, 2022 · In this short article, we first define the probing classifiers framework, taking care to consider the various involved components. Feb 29, 2024 · Finally, inspired by~\citet {choi2023understanding} that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with mutual information to investigate the dynamics of trustworthiness during pre-training. LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states Luis Ibanez-Lissen1, Lorena Gonzalez-Manzano1, Jose Maria de Fuentes1,2, Nicolas Anciaux2, and Joaquin Garcia-Alfaro3 Concept Depth is used to analyze the comprehension ability of Large Language Models (LLM) and the difficulty of a concept's understanding. Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. It is similar to representation reading in that it also learns a linear direction in activation space related to the concept. By com-paring these judgments with the actual labels, we Probing for truthfulness. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Then we summarize the framework’s shortcomings, as well as improvements and advances. Here is my understanding of linear probing. Dec 21, 2022 · This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. e. The improvement manifests in introducing a non-linear multi-token probing and multi-token intervention: Non-Linear ITI (NL-ITI Dec 1, 2024 · We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Probing involves training simple, auxiliary models, called probes, to predict specific properties of interest directly from the LLM's internal representations. Accuracy results are ‘smoothed’ between neighboring attention heads (lower standard deviation). , 2023b) and unsuper- Jan 10, 2025 · LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states Luis Ibanez-Lissen 11 Lorena Gonzalez-Manzano 11 Jose Maria de Fuentes 1122 Nicolas Anciaux 22 Joaquin Garcia-Alfaro 33 The transformer-heads library makes it easy to add one or multiple heads to an open source LLM such as LLaMA or Mistral. Apr 5, 2023 · Two standard approaches to using these foundation models are linear probing and fine-tuning. In the dictionary problem, a data structure should maintain a collection of key–value pairs subject to operations that insert or delete pairs from the collection or that search for the value associated with a given key. 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. This additional classifier is trained to predict specific linguistic properties or features, such as part-of-speech tags, syntactic structures, sentiment, or named entities. We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. To understand what these representations capture, we turn to a technique known as probing. The improvement manifests in introducing a non-linear multi-token probing and multi-token intervention: Non-Linear ned LLM with patched time-series data. （Probe也可以称之为probing classifiers, diagnostic classifiers, auxiliary prediction tasks）Probe探究了神经网络的内部机制如何对auxiliary linguistic tasks (or probe tasks, or ancillary tasks)进行分类。 Jan 2, 2024 · Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. Improving our understanding of the structure of LLM truth representations also improves our ability to extract LLM beliefs: based on geometrical considerations, we introduce mass-mean probing1, a simple, optimization-free probing technique which generalizes better and identifies more causally implicated directions than other probing techniques. Probing LLM Pre-training Dynamics in Trustworthiness The linear probe accuracy on five trustworthiness dimensions for the first 80 pre-training checkpoints. We use probing techniques🔍 on each layer's embedding to detect the layer accuracy, F1-score, and AUC of the classification task. Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem. To understand why this occurs, we employ (nearly) linear probing to demonstrate a strong connection between the observed correlation and how the model internally encodes knowledge — whether it is linearly encoded in the hidden embeddings of entity names or distributed across other token embeddings in the training text. The correlation between the perplexity predicted by graph probing and the ground-truth perplexity reflects how well LLM performance can be inferred from ne 2 days ago · Probing LLM s for Joint Encoding of Linguistic Categories Giulio Starace, Konstantinos Papakostas, Rochelle Choenni, Apostolos Panagiotopoulos, Matteo Rosati, Alina Leidinger, Ekaterina Shutova Important: The Anthology treat PDFs as authoritative. Prior work has largely focused on linear probing (Logistic Regression or Difference-in-Means) and dimensionality reduction techniques (most notably PCA) in order to obtain concept vectors that can be used for detection and guidance. Abstract In this work, we explore LLM’s internal representation space to identify attention heads that contain the most truthful and accurate information. Fine-tuning updates all the parameters of the model. Aug 22, 2024 · Creating and defining metrics to evaluate LLM-generated code Developing negative-probing methods and a benchmark to evaluate a given LLM’s performance as a judge Evaluating the capability of deepseek-coder-33B as a judge by using an agent-based approach Mar 16, 2024 · "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者一个全连接层）来测试模型学习到的特征的质量（要冻结encoder的所有参数）。 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. 6k次，点赞9次，收藏14次。本文探讨了自监督学习中预训练模型应用于下游任务的两种常见方法：full fine-tuning和linear probing。full fine-tuning涉及更新所有模型参数，有时会冻结部分卷积层；而linear probing则仅更新最后一层线性层，保持预训练模型的特征层不变，通过监督数据训练分类器 Linear Probing Linear probing is a simple open-addressing hashing strategy. This method has been extensively analyzed and enhanced [50, 46, 16, 26]. Dec 1, 2024 · We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. The high probing accuracy suggests that LLMs in early pre-training can already distinguish conc pts in each trustworthiness dimension. Mar 25, 2024 · Researchers find large language models use a simple mechanism to retrieve stored knowledge when they respond to a user prompt. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. PALP inherits the scalability of linear probing and the capability of enforcing language models to derive more meaningful representations via tailoring input into a more conceivable form. Aug 14, 2024 · They then trained an LLM on the solutions, but without demonstrating how the solutions actually worked. Quadratic probing helps distribute keys more evenly throughout the hash table, reducing the likelihood of clustering. Note the score layer, added to the general Llama architecture during fine-tuning using LoRa. This decomposition highlights the importance of the linear head norm alongside the prediction accuracy at the start of the FT stage. Mar 28, 2023 · Every component (layer, head and neuron) reads its input from the residual stream with a linear map, and writes it output by adding it to the residual stream, which is a really nice structure. E. The improvement manifests in introducing a non-linear multi-token probing and multi-token intervention: Non-Linear Probes: Our baseline linear probes incorporated a linear projection succeeded by a sigmoid function. Sep 19, 2024 · 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 used for future token prediction. We propose graph probing to study the dependence between LLM performance and ne ral topology. 5. , only the output layer is unfrozen), followed by full fine-tuning (all the layers and PE T com Feb 24, 2024 · We find some probing methods with impressive generalisation, that appear to be measuring something more than truth. A probing experiment also requires a probing model, also known as an auxiliary classifier. May 27, 2024 · In this paper, we analyze the training dynamics of LP-FT for classification tasks on the basis of the neural tangent kernel (NTK) theory. Mar 27, 2024 · In this work, we explore LLM's internal representation space to identify attention heads that contain the most truthful and accurate information. The improvement manifests in introducing a non-linear multi-token probing and multi-token intervention: Non-Linear ITI (NL-ITI), which significantly enhances perfor-mance on evaluation benchmarks. ipynb at main · center-for Oct 15, 2023 · MONITOR computes the distance between the probability distributions of a valid output and its counterparts produced by the same LLM probing the same fact using different styles of prompts and this http URL on a comprehensive range of 12 LLMs demonstrate the effectiveness of MONITOR in evaluating the factual reliability of LLMs while maintaining Sep 21, 2023 · In this work, we curate high-quality datasets of true/false statements and use them to study in detail the structure of LLM representations of truth, drawing on three lines of evidence: 1. 原理 训练后，要评价 模型 的好坏，通过将最后的一层替换成 线性 层。 Jul 8, 2025 · linear probing 是在适配下游任务时，冻住预训练模型，对其参数不进行更新，只对模型最后一层的线性层进行参数更新 线性探测一般用于检验预训练模型的好坏 Poster Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective Akiyoshi Tomihari · Issei Sato Jun 4, 2024 · With this in mind, I evaluated a lie detector trained with a state-of-the-art, white box technique - probing an LLM’s activations during production of facts/lies - and found that it had high sensitivity but low specificity. Probing across layers: One way this is nice is that we can immediately get a foothold into understanding how the world model is computed. Which method does better? Oct 10, 2023 · In this work, we use high-quality datasets of simple true/false statements to study in detail the structure of LLM representations of truth, drawing on three lines of evidence: 1. 2 days ago · 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 preferences. We are the first to observe a similar two-phase phenomenon: fitting and compression~\citep {shwartz2017opening}. 3. , 1986) that take neural topology as input to predict its corresponding language generation performance, as illustrate Insert the key into the first available empty slot. In Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024, pages 1–7, Torino, Italia. Specifically, we adopt simple linear or multi-layer perceptrons (MLP) probes (Rumelhart et al. Our experiments show that constructing and optimizing against this surrogate reward function reduces sycophantic behavior in multiple open-source LLMs. For insertion: - We hash to a certain position. The model generates a response for each sample, from which we infer a judgment, ca egorizing it as either "Yes" or "No". We conjecture that one cannot make vision LLMs understand visual concepts fully hierarchical until LLMs possess corresponding taxonomy knowledge. g. Aug 20, 2025 · YOLOE supports 2 modes of training, “full finetuning” and “linear probing”. 1 Predictability LLM families. When a collision occurs (i. 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模型所提取的特征，假设这些特征已经在某种程度上对数据的语义等信息进行了有效的编码。 线性 Nov 30, 2023 · We explore two instantiations of Aug-imodels in natural-language processing: Aug-Linear, which augments a linear model with decoupled embeddings from an LLM and Aug-Tree, which augments a decision Figure 2: Probing accuracy for each attention head of the LLM on TruthfulQA dataset for linear probing (ITI) – bottom, and non-linear probing (NL-ITI) – top. Mar 3, 2025 · These linear probes allow us to visualize, interpret, and monitor ideological stances implicitly adopted by an LLM as it generates open-ended responses. 2 days ago · Probing Large Language Models from a Human Behavioral Perspective. Nov 20, 2025 · Third, LLM-QA performance correlates well with linear probing, and the multi-task encoder proves most effective for grounding LLM reasoning. Linear probing freezes the foundation model and trains a head on top. We conduct ex-tensive probing experiments using layer-wise representations across various LLM families (Gemma, LLaMA, Qwen) on various datasets spanning the three domains of tasks. ral Topology. Therefore, to further uncover the hidden pos-sibilities of pre-training, we extract steering vectors from a LLM’s pre-training checkpoints Jun 4, 2024 · With this in mind, I evaluated a lie detector trained with a state-of-the-art, white box technique - probing an LLM’s activations during production of facts/lies - and found that it had high sensitivity but low specificity. Visualizations of LLM true/false statement representations, which reveal clear linear structure. 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 reveals a two-phase trend regarding trustworthiness. In Jul 8, 2025 · 文章浏览阅读3. Therefore, Jul 8, 2025 · 得到的准确率就是 Linear Probing Accuracy。 这个数值直接反映了预训练 ResNet-50 提取的特征在 CIFAR-10 任务上的 线性可分离程度 和 迁移潜力。 关键点总结： 冻结一切： 预训练模型的所有层（卷积层、池化层、BN层、甚至原始的全连接层）的参数都 不允许更新。 Toolkit for attaching, training, saving and loading of new heads for transformer models - transformer-heads/notebooks/gpt2/linear_probe. While verbalized uncertainty involves prompting the LLM to express its confidence in its explanations, probing uncertainty leverages perturbations as means to quantify the uncertainty. Feb 16, 2025 · Our results suggest linear probing offers an accurate, robust and computationally efficient approach for LLM-as-judge tasks while providing interpretable insights into how models encode judgement-relevant knowledge. Or you could do linear probing to figure out where in you LLM Oct 28, 2023 · Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. 2. Jun 26, 2023 · This paper proposes prompt-augmented linear probing (PALP), a hybrid of linear probing and ICL, which leverages the best of both worlds. You can find an example of generative fine-tuning here. , when two keys hash to the same index), linear probing searches for the next available slot in the hash table by incrementing the index until an empty slot is found. Finally, we demonstrate that by applying linear interventions to these attention heads, we can steer the model outputs toward a more liberal or conservative stance. Forcing linear probes on top of LLM hidden layer activations to have a Feb 6, 2025 · 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 analysis decomposes the NTK matrix into two components. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. Oct 12, 2023 · Specifically, researchers have tried "probing" the internal neural network representations of LLMs to see if directions or vectors corresponding to true statements can be identified. Article LUMIA: Linear Probing for Unimodal and MultiModal Membership Inference Attacks Leveraging Internal LLM States Authors: Luis Ibanez-Lissen , Lorena Gonzalez-Manzano , Jose Maria de Fuentes Probing LLM Pre-training Dynamics in Trustworthiness The linear probe accuracy on five trustworthiness dimensions for the first 80 pre-training checkpoints. 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. For example, a probe may attempt to classify if the LLM representation of the statement "The sky is green" is oriented closer to true or false statements. Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. Oct 9, 2024 · We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. period of an LLM be utilized to enhance its trust- worthiness after pre-training? Abstract Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps—missing or outdated information in LLMs—might always persist given the evolv-ing nature of knowledge. Finally, using a machine learning technique called “probing,” they looked inside the model’s “thought process” as it generates new solutions. In this work, we study approaches to identify LLM knowledge gaps and abstain from answering questions when knowledge gaps are present. 5-0. You can find an example of predictive fine-tuning here. 676$ 6DUFDVP &RLQIOLS ,0'E 6WUDWHJ\4$ 3: Analysis diagrams of Section 5. Dec 2, 2024 · Introduction Probing tasks are essential tools for understanding the inner workings of Tagged with llm, 75daysofllm. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal Apr 25, 2024 · Using linear probes to dissect internal LLM embeddings to check for a hint of an internal world model. Nov 1, 2023 · We fill this gap by offering a systematic study on prompt probing for multimodal LLMs, examining various factors for their understanding of prompts. Existing model interpretability research (Tenney et al. To insert an element x, compute h(x) and try to place x there. Sep 20, 2025 · 【Linear Probing | 线性探测】深度学习 线性层 1. 1. We further developed the Inference Time Intervention (ITI) framework, which lets bias LLM without the need for fine-tuning. 1 Position-Based Probing In P-probing, we feed biography entries into a pretrained model and train an additional linear classifier on the model’s final hidden layer to predict six target attributes (e. If that position already has a value, we linearly increment to the next position, until we encounter an Oct 5, 2016 · Neural network models have a reputation for being black boxes. The results above ask more questions than they answer: Why do probes not generalise perfectly? Do they overfit to the interference from other features? Feb 19, 2024 · End-to-end fine-tuning 和 Linear probing 是两种用于迁移学习或微调深度学习模型的策略，它们在方法和应用上有一些区别： End-to-end Fine-tuning（端到端微调）： 方法：在迁移学习中，将 预训练模型 的所有层都解冻，并使用新的数据集进行端到端的微调。通常，所有层的权重都被更新。 应用：适用于目标 To this end, we propose two novel metrics — Verbalized Uncertainty and Probing Uncertainty — to quantify the uncertainty of generated explanations. This is evidence for a generalised notion of truth in Llama-2-13B-chat. 5B. Our data and code will be openly released in the future. , university, major, etc. Poster in Workshop: Socially Responsible Language Modelling Research (SoLaR) Linear Probe Penalties Reduce LLM Sycophancy Henry Papadatos · Rachel Freedman Keywords: [ LLM ] [ alignment ] [ reward model ] [ sycophancy ] Interestingly, finetuning a vision LLM using our VQA tasks reaffirms LLMs' bottleneck effect to some extent because the VQA tasks improve the LLM's hierarchical consistency more than the vision LLM's. Linear probing offers simplicity and low memory overhead but may suffer from clustering. This gives us an idea where ranking related information is stored in the model. Jul 14, 2024 · Probing Linear Probing attempts to learn a linear classifier that predicts the presence of a concept based on the activations of the model [33]. you could finetune your LLM that originally only does causal language modelling to perform well on a regression task. Probing accuracy for each attention head of the LLM on TruthfulQA dataset for linear probing (ITI) – bottom, and non-linear probing (NL-ITI) – top. Other authors have trained probes to classify truthfulness from LLM activations, using both logistic regression (Azaria & Mitchell, 2023; Li et al. Both ways are valid collision resolution techniques, though they have their pros and cons. ). Probing Search This project contains code for the probing experiments in: Probing BERT for Ranking Abilities. Figure 2: Probing accuracy for each attention head of the LLM on TruthfulQA dataset for linear probing (ITI) – bottom, and non-linear probing (NL-ITI) – top. This probing provides insight into how these attributes are encoded during pretraining. Feb 28, 2025 · We address these challenges by systematizing LLM social bias probing using actionable insights from social sciences. (b) Forecasting fine-tuning, which starts with linear probing (i. what are the best practices for choosing which mode to use, is it simply a choice based on if we are compute restricted? I was thinking that for domain adaptation, (COCO style camera angle domain to AERIAL domain for example) full fine-tuning would be required to update the backbone to handle the new domain. We demonstrate how this Nov 3, 2023 · Using linear probing, we unravel that such augmentation forces the model to store knowledge about a person in the token embeddings of their name rather than other locations. Particularly, graph probing achieves 0. We then introduce EcoLevels - a framework that helps (a) determine appropriate bias probes, (b) reconcile conflicting findings across probes, and (c) generate predictions about bias generalization. If that spot is occupied, keep moving through the array, wrapping around at the end, until a free spot is found. Here we define a simple linear classifier, which takes a word representation as input and applies a linear transformation to map it to the label space. We are the first to observe a similar two-phase phenomenon: fitting and compression. Models during the early stages of pre-training can already encode trustworthiness well. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. 95 in ρp and ρs, and Qwen2. This helps us better understand the roles and dynamics of the intermediate layers. Sep 2, 2024 · Figure 1. cuixuiem vyoaa mduawma mvb gtrkjd kyujc cieus ifcyvi lzb rrhb zva ahji yqcbuixl oaspfk ouyti