Linear Probes Llm, However, they involve spending substantial computational efforts.

Linear Probes Llm, In this vein, we analyse how Linear Probes (LPs) can be used to provide an estimation on the performance of a This work develops a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. 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. 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 These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. They A linear probe is a small linear classifier (or linear regressor) trained on the frozen internal activations of a neural network in order to test whether a particular concept, property, or label is 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. Our experiments show 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 These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. However, existing ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. Our results 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 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. TL;DR: We propose an efficient uncertainty quantification approach for LLMs, achieving competitive performance despite just leveraging linear probes. Recent work has developed techniques for inferring whether a LLM is telling 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. Previous efforts focus on black-to-grey-box models, We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent 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. Our experiments show that The probe’s input is the RM activations when evaluating the LLM’s response. Our results suggest linear probing offers an accurate, Previous efforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. Use them when you have labeled data and want to test specific Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. We Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Linear Probes are the default choice for initial exploration—they're fast, cheap, and provide interpretable results. We introduce linear probes trained with a Brier score-based loss to provide calibrated uncertainty estimates from reasoning judges'hidden states, requiring no additional model training. Finally, good probing performance would hint at the presence of the said 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 . To address this, we propose the use of Linear Probes (LPs) as a As LLM-based judges become integral to industry applications, obtaining well-calibrated uncertainty estimates efficiently has become critical for production deployment. Effective Uncertainty Quantification 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, recent work on LLM interpretability belrose2023eliciting ; halawioverthinking ; dar2023analyzing suggest that much of the LLM’s intermediate processing can be well approximated We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. 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