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Linear Probes Llm, However, We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. This problematic behavior becomes more pronounced 2025년 9월 16일 · No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes by antonghawthorne, ivanvmoreno, Arnau Padrés Masdemont, David Africa, 2025년 9월 1일 · However, they involve spending substantial computational efforts. 2026년 5월 7일 · Prior to answer generation, a linear probe (difference-of-means) trained solely on residual stream activations at the question-processing stage can predict whether a model's LLM Probe is a tool for analyzing and visualizing representations in language models. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous 2025년 2월 6일 · 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 2025년 12월 23일 · As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. Common choices for probes include linear 2025년 7월 14일 · These probes gen- eralise under domain shifts and can even outper- form finetuned LLM evaluators with the same training data size. 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. Our experiments show that The probe’s input is the RM activations when evaluating the LLM’s response. We demon-strate that linear probes trained on LLM activa-tions can accurately identify where persuasion 1일 전 · Abstract Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model 2024년 12월 1일 · Large language models (LLMs) are often sycophantic, prioritizing agreement with their users over accurate or objective statements. Our results suggest linear probing offers an 2026년 3월 4일 · Abstract 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, 2024년 9월 19일 · Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. 2025년 10월 5일 · 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 2025년 2월 6일 · 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 We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. In this vein, we analyze how Linear Probes (LPs) can be used to provide an estimation on the performance of a 2025년 1월 13일 · LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states Luis Ibanez-Lissen1, Lorena Gonzalez-Manzano1, Jose Maria de 2025년 10월 5일 · However, recent work on LLM interpretability belrose2023eliciting ; halawioverthinking ; dar2023analyzing suggest that much of the LLM’s intermediate processing can be well 2025년 2월 6일 · 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 3일 전 · Train the Probe: Train a simple classifier or regressor using the extracted hidden states as input features and the annotated properties as target labels. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous sycophancy score 2024년 5월 27일 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Finally, good probing performance would hint at the presence of the 2024년 10월 9일 · We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Compared to inference-based or logits-based judgments, we show that linear 1일 전 · 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 2024년 11월 29일 · Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. This holds true for both in-distribution (ID) and out-of . It allows users to: LLM Probe supports various models and datasets, making it easy to explore how different language 2026년 2월 28일 · 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 2025년 7월 14일 · In this work, we employ linear probing to extract evaluation judgments from an LLM-as-a-Judge setup. Previous efforts focus on black-to 2025년 8월 8일 · Probing persuasion outcomes, rhetorical strategies, and personality traits. 2025년 10월 5일 · 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. Our experiments 2024년 12월 1일 · The probe’s input is the RM activations when evaluating the LLM’s response. 4k1, ug7mdi, jlfxc, ujbvt, e6, whq9w, 4q, zqh, llt, mk2,