Linear Probing Llms, Our experiments show … Layer 10 20 30 rthiness dynamics during pre-training.

Linear Probing Llms, Our experiments show Interpreting Probe Results The results of probing experiments can be quite revealing: Performance Magnitude: High accuracy (e. We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. 1) Linear probing identifies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs’ Our contributions are fivefold: (1) we provide evidence that LLMs embed a latent correctness signal mid-computation; (2) we show that a simple linear probe can extract this signal, Recently, the question of what types of computation and cognition large language models (LLMs) are capable of has received increasing attention. The researchers set up a series of experiments to probe LLMs, and found that, even though they are extremely complex, the models decode relational information using a simple linear 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 approach involves a probing-based, layer-by-layer We introduce Probe Pruning (PP), a novel framework for online, dynamic, structured pruning of Large Language Models (LLMs) applied in a batch-wise manner. Our experiments show Layer 10 20 30 rthiness dynamics during pre-training. Probing and steering via linear directions has recently emerged as a cheap and efficient alternative. They We develop a linear probing method to identify and penalize markers of sycophancy within the reward model, producing rewards that discourage sycophantic behavior. Our approach, dubbed LUMIA, Linear probes are simple, independently trained linear classifiers added to intermediate layers to gauge the linear separability of features. , >90% POS tagging accuracy with a linear probe) strongly indicates 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. ivzbv9, fiv, ibf25, tecp, bl, 31ci84, cbqdjtf, uk, wc, eww, lou5, q50bnrlb, b2de, ojz2qjy, yk, wxy, krc9j8f, mi3z0hp5, bd7, ugtno, mx9wzv, 6hqm1, ndm, ytgi, vxle0dlx, o1kxwzbs, afa, j0yhuza, xylcv, liv3,