Linear probes ai github. Increase of the probe's accuracy on non-related features w.

Linear probes ai github Lightly SSL is a computer vision framework for self-supervised learning. We propose a novel approach that meets the requirements of real-world scenarios. Systematic experiments Using a linear classifier to probe the internal representation of pretrained networks: allows for unifying the psychophysical experiments of biological and artificial systems, is not limited to measuring the contrast sensitivity function of a network, and it can be used for other psychophysics. Contribute to t-shoemaker/lm_probe development by creating an account on GitHub. The probe will be trained from hidden representations from a specific layer of the BERT model. Creation of Sleeper Agents and Probes. Can now run, e. - anthropics/sleeper-agents-paper Apr 2, 2024 · In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. json --batch_size=64 Evaluating AlexNet features at various depths. And +20M params. A Simple Episodic Linear Probe Improves Visual Recognition in the Wild. Jul 29, 2022 · Thank you for your amazing paper, I am trying to evaluate CLIP with a linear-probe on ImageNet, but wish to save some of the compute needed for the sweep required to optimize the C hyperparameter f February 2025 Our paper Distilling Datasets Into Less Than One Image was accepted to TMLR. Contribute to mahmoodlab/UNI development by creating an account on GitHub. D. ai. It has commentary and many print statements to walk you through using a single probe and performing a single intervention. Supported optimization methods Dec 16, 2024 · Setting up the Probe Before we define the probing classifier or probe, let’s set up some utility functions the probe will use. Additionally, the adversarial prompt can be optimized for naturalness (high likelihood). Dec 4, 2024 · Train linear probes on neural language models. CVPR 2024 paper: LP++: A Surprisingly Strong Linear Probe for Few-Shot CLIP Introduction LP++ is a simple generalization of the standard linear-probe classifier, which integrates text knowledge: We express the linear classifier weights as learnable functions of the text embeddings, with class-wise multipliers blending image and text features. Contribute to Kojk-AI/sleeper-agent-probe development by creating an account on GitHub. , clip_benchmark --dataset=cifar10 --task=linear_probe --pretrained=laion400m_e32 --model=ViT-B-32-quickgelu --output=result. The train_test_chess. a transformer pretrained to do causal language modelling Feb 5, 2025 · Detecting Strategic Deception Using Linear Probes: Paper and Code. We Our method employs a linear probe within the reward model to quantify the extent of sycophancy in the AI’s responses. We introduce a CLass-Adaptive linear Probe (CLAP) objective, that constraints the learned prototypes to retain prior zero-shot knowledge adaptely based only on the few support shots, and uses an homogeneus learning configuration accross tasks. Jan 3, 2024 · A Chess-GPT Linear Emergent World RepresentationA Chess-GPT Linear Emergent World Representation Introduction Note: This work has since been turned into a paper accepted to the Conference on Language Modeling, but the average reader will probably prefer the blog post. Apr 5, 2023 · Ananya Kumar, Stanford Ph. linear probe. Hallucinations, which are plausible-sounding but factually incorrect and arbitrary model generations, present a major challenge to the practical adoption of LLMs To visualise probe outputs or better understand my work, check out probe_output_visualization. And Gated MLPs. the input sample but related to the target sample. For Imagenet with 1M+ images in the training split it was quite slow and requires huge memory especially considering the hyperparameter sweep for the L2 regularization term (C). Learn about the construction, utilization, and insights gained from linear probes, alongside their limitations and challenges. We demonstrate how this Common approaches for model adaptation either update all model parameters or leverage linear probes. Linear probes with attention weighting. Among the many recent developments in ML, there were [WACV 2026] An extremely simple method for validation-free efficient adaptation of CLIP-like VLMs that is robust to the learning rate. January 2025 Two papers accepted to ICLR 2025: Unsupervised Model Tree Heritage Recovery and Deep Linear Probe Generators for Weight Space Learning. Yuanzhi Liang, Linchao Zhu, Xiaohan Wang, Yi Yang. Forcing certain continuations of the prompt. Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. E. This project extends the Virtue Probes methodology to the Empathy in Action (EIA) benchmark, investigating whether empathic behavior can be detected and steered through linear directions in transformer activation space. We then modify the reward model to penalize responses based on their sycophancy score. Dec 1, 2024 · The linear probe functions as a diagnostic tool that identifies specific neural patterns associated with sycophantic behavior in LLMs. ipynb. We use linear classifiers, which we refer to as "probes", trained entirely independently of the model itself. Technically, it analyzes the model's internal representations to detect when it's being overly agreeable rather than truthful. GitHub is where people build software. g. ipynb at main · center-for Apr 23, 2024 · In this post we present "defection probes": linear classifiers that use residual stream activations to predict when a sleeper agent trojan model will choose to "defect" and behave in accordance with a dangerous hidden goal. By combining the speed of ripgrep with the code-aware parsing of tree-sitter, Probe delivers precise results with complete code blocks—perfect for large codebases and AI-driven development workflows. Contribute to Hyomin-Seo/Deep-Learning development by creating an account on GitHub. We propose to monitor the features at every layer of a model and measure how suitable they are for classification. Optionally concatenating the adversarial prompt with a prefix and/or postfix string. This document is part of the arXiv e-Print archive, featuring scientific research and academic papers in various fields. We find that optimizing against this augmented reward model successfully reduces sycophantic behavior in multiple large open-source LLMs. CLIP-like model evaluation. Contribute to EleutherAI/attention-probes development by creating an account on GitHub. Accepted by CVPR 2022 VrR-VG: Refocusing Visually-Relevant Probe is an AI-friendly, fully local, semantic code search tool designed to power the next generation of AI coding assistants. We test two probe-training datasets, one with contrasting instructions to . Feb 19, 2023 · Inference model-checkpoint generated from eval_linear_probe #65 Closed sakshamsingh1 opened this issue on Feb 19, 2023 · 2 comments Sep 1, 2023 · Now, append a linear probe to the last layer of the frozen encoder and discard the decoder. Image 2: Caption: Above is the showing the results from an experiment where I mesured the success of linear probes to distinguish sycophancy, however I had results that didn't make sense. Oct 25, 2024 · This guide explores how adding a simple linear classifier to intermediate layers can reveal the encoded information and features critical for various tasks. AI models might use deceptive strategies as part of scheming or misaligned behaviour. We test two probe-training datasets, one with contrasting instructions to 5. Evaluating AlexNet features at various depths. Feb 5, 2025 · AI models might use deceptive strategies as part of scheming or misaligned behaviour. They benefit from a larger number of heads, but increasing the number of heads leads to higher attention weight entropy. Dec 12, 2024 · We released a new open source byte-pair tokenizer that is faster and more flexible than popular alternatives. [ICPR 2024] CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP - wzczc/CLIP-AGIQA Feb 3, 2025 · Produced as the capstone project for AI Safety Fundamentals Course Oct 2024 - Jan 2025 Overview Anthropic's paper Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training [1] demonstrated that it is possible to create a misaligned AI that is resilient to our current best safety practices (RLHF, SFT, Adversarial training, etc. Contains random samples referenced in the paper "Sleeper Agents: Training Robustly Deceptive LLMs that Persist Through Safety Training". Geometry of Sycophancy: The dataset below has the input datasets consist of sentence long indications for sycophancy instead of single word answers. Analysing Adversarial Attacks with Linear Probing Goal See what kind of features (if any) adversarial attacks find. Documentation Github Discord For a commercial version with more features, including Docker support and pretraining models for embedding, classification, detection, and segmentation tasks with a single command, please contact sales@lightly. Contribute to Johnny221B/LLM-program development by creating an account on GitHub. Contribute to yukimasano/linear-probes development by creating an account on GitHub. t. Jun 17, 2024 · The probes seem to detect the concepts better in later layers. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline, in which the linear classifier We propose semantic entropy probes (SEPs), a cheap and reliable method for uncertainty quantification in Large Language Models (LLMs). This is a new template based on a linear probes / steering vector task. Vision Transformers Needs Registers. Yuanzhi Liang, Qianyu Feng, Linchao Zhu, Li Hu, Pan Pan, Yi Yang. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. Apr 23, 2024 · 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 applications such as measurement tampering. In this work, we aim to study parameter-efficient model adaptation strategies for vision transformers on the image classification task. Accepted by CVPR 2022 (Score 1/2/2) SEEG: Semantic Energized Co-speech Gesture Generation. Contribute to gavinratcliff/sleeper-agents-repro development by creating an account on GitHub. Jul 13, 2025 · Fine-tuning code for CLIP models. Contribute to zer0int/CLIP-fine-tune development by creating an account on GitHub. Oct 5, 2016 · Neural network models have a reputation for being black boxes. Some discussions can be found in: Hyperparameter sweep in Evaluation (linear probe) #39 (comment) Evaluation with ImageNet #64 (comment) Linear-probe evaluation The example below uses scikit-learn to perform logistic regression on image features. We contribute the following new insights: We first show that trained linear probes can accurately map the activation vectors of a GPT-model, pre-trained to play legal moves in the game Othello, to the current state of the othello board. The project also releases the computationally expensive activation data to stimulate further AI safety research. py script can be used to either train new linear probes or test a saved probe on the test set. The primary focus of the paper is to investigate the application of linear probing classifiers for modifying the internal states of a chess-playing GPT model trained on UCI move sequences. Resolves hash table collisions using linear probing, quadratic probing, and linear hashing. Tiny modality gap ensues! - zer0int/CLIP-fine-tune-registers-gated Nov 12, 2023 · Hi, we used full-batch linear regression using L-BFGS. This helps us better understand the roles and dynamics of the intermediate layers. Increase of the probe's accuracy on non-related features w. The appended classifier is then trained on 4000 labeled samples of the 'train' split (another 1000 are used for training validation) and evaluated on the 'test' split. Contribute to stvngo/Algoverse-AI-Model-Probing development by creating an account on GitHub. Feb 6, 2025 · Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. Real-time global illumination using screen-space information for Unity HDRP - cdrinmatane/SSRT3 Customer models Upload your model files in linear_probe/models/ABC. - microsoft/TaskTracker Dec 16, 2024 · Setting up the Probe Before we define the probing classifier or probe, let’s set up some utility functions the probe will use. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal reasoning is misaligned. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. student, explains methods to improve foundation model performance, including linear probing and fine-tuning. Optimized for efficient time and space complexity. - astra-vision/ProLIP Deep Learning. December 2024 Our workshop Neural Network Weights as a New Data Modality will take place at ICLR 2025. There is also a second blog post, Manipulating Chess-GPT’s World model. Toolkit for attaching, training, saving and loading of new heads for transformer models - transformer-heads/notebooks/gpt2/linear_probe. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal reasoning is misaligned. The process works in three main steps: 1) The probe learns to recognize patterns in the AI's internal states that correlate with A linear probe used to get an understanding of the information processing in a transformer architecture A head to be finetuned jointly with the weights of a pretrained transformer model to perform a completely different kind of task. All data structures implemented from scratch. Upload the corresponding config files in linear_probe/configs/model_cfg/. Aug 1, 2025 · Attention probes are mostly comparable to mean- or last-token probes, depending on which is better for a given dataset. Nov 29, 2024 · Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Setup Model: ViT (CLIP) Currently, supported adversarial optimization targets are: Forcing linear probes on top of LLM hidden layer activations to have a certain score. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. This is in contrast to the utilization of the non-linear MLP as probes. May 1, 2025 · AI models might use deceptive strategies as part of scheming or misaligned behaviour. Contribute to LAION-AI/CLIP_benchmark development by creating an account on GitHub. Written in C++. To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining This repository contains the code and probe model tensors for the paper "Performance Envelopes of Linear Probes for Latent Representation Edits in GPT Models". r. TaskTracker is an approach to detecting task drift in Large Language Models (LLMs) by analysing their internal activations. config, ABC is your model name. Model Probing and Experimentation . We've also built a whole platform on top, with additional features for active GitHub is where people build software. It provides a simple linear probe-based method and a more sophisticated metric learning method to achieve this. ) -- specifically a model that will demonstrate performance-envelopes-icmla-2024 Public Repository for the paper titled "Performance Envelopes of Linear Probes for Latent Representation Edits in GPT Models" Python 3 Mar 6, 2025 · Pathology Foundation Model - Nature Medicine.