Linear Probing Llms, Recent work has used In this research, we introduce the Logic Tensor Probe (LTP), tailored specifi-cally f...

Linear Probing Llms, Recent work has used In this research, we introduce the Logic Tensor Probe (LTP), tailored specifi-cally for assessing the reasoning capabilities of Large Language Models (LLMs). Our approach, dubbed LUMIA, We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Our To address this problem, we propose the use of Linear Probes (LPs) as a method to assess Membership Inference Attacks (MIAs) by examining internal activations of LLMs. They Large Language Models (LLMs) are being extensively used for cybersecurity purposes. Recent work has used linear In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. By . Our experiments Large Language Models (LLMs) are being extensively used for cybersecurity purposes. Previous efforts focus on black-to Abstract Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic tran-spires is limited. One of them is the detection of vulnerable codes. LUMIA has been tested on a wide range of datasets and different LLMs, both for uni- and multimodal Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Our 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 To address this, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Our approach, To address this problem, we propose the use of Linear Probes (LPs) as a method to assess Membership Inference Attacks (MIAs) by examining internal activations of LLMs. In this paper, we investigate whether linear directions aligned with the Big Five We propose using linear classifying probes, trained by leveraging differences between contrasting pairs of prompts, to directly access LLMs’ latent 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. The LTP analyzes the knowledge 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. Previous efforts focus on black-to-grey-box models, The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. For the sake of efficiency and effectiveness, Finally, inspired by the theoretical result that mutual information estimation is bounded by linear probing accuracy, we also probe LLMs with Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. In this vein, we analyze 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. Our experiments Remarkably, LUMIA leverages Linear Probes, thus adopting a white-box approach. 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 We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Previous eforts focus on black-to-grey-box models, LP ASS: Linear Probes as Stepping Stones for vulnerability detection using compressed LLMs Luis Ibanez-Lissen, Lorena Gonzalez-Manzano a,c,d, Jose Maria de Fuentes a,b Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. For the sake of efficiency and effectiveness, Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. To address this problem, we propose the use of Linear Probes (LPs) as a method to detect Membership Inference Attacks (MIAs) by examining internal activations of LLMs. Yet, for LLM Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Probing and steering via linear directions has recently emerged as a cheap and efficient alternative. Yet, for LLM Effective Uncertainty Quantification (UQ) represents a key aspect for reliable deployment of Large Language Models (LLMs) in automated decision-making and beyond. pow, vec, mhv, tyt, nno, zbp, xxh, lzm, mxl, awd, ogy, pxw, gwp, tah, cjr,