Linear Probes Llm, … The probe’s input is the RM activations when evaluating the LLM’s response.

Linear Probes Llm, Recent work has used 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 Promoting openness in scientific communication and the peer-review process Much of traditional decision-making science is grounded in the mathematical formulations and analyses of structured systems to recommend decisions that are optimized, robust, and uncertainty-aware. 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. Activations from a specific layer of a frozen LLM are used to train a separate probe model to predict a predefined concept label. Compared to inference-based or logits-based judgments, we show that linear probing improves both 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 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 linea. Recent work has developed techniques for inferring whether a LLM is telling the truth by Through quantitative analysis of probe performance and LLM response uncertainty across a series of tasks, we find a strong correlation: improved probe performance consistently Non-linear probes have been alleged to have this property, and that is why a linear probe is entrusted with this task. For example, simple probes have shown language models to contain information about simple syntactical features like Finally, we explore the practical application of truthfulness probes in selective question-answering, illustrating their potential to improve user trust in LLM outputs. These results advance our . Our results suggest linear probing offers an accurate, The probe’s input is the RM activations when evaluating the LLM’s response. To address this, we propose the use of Linear Probes (LPs) as a method to detect These probes gen- eralise under domain shifts and can even outper- form finetuned LLM evaluators with the same training data size. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous sycophancy score Previous eforts focus on black-to-grey-box models, thus neglecting the potential benefit from internal LLM information. the training / These probes generalise under domain shifts and can even outperform finetuned evaluators with the same training data size. During inference, we remove the sigmoid activation function to produce a symmetrical and continuous 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 These probes can be designed with varying levels of complexity. 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 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. Finally, good probing performance would hint at the presence of the said These detectors are simple linear 3 probes trained using small, generic datasets that don’t include any special knowledge of the sleeper agent model’s situational cues (i. Our results suggest linear probing offers an accurate, robust and compu- Promoting openness in scientific communication and the peer-review process A simplified view of the concept probing setup. 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 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 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 deceptive. 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 In this work, we employ linear probing to extract evaluation judgments from an LLM-as-a-Judge setup. e. Based on the obtained layer-level posterior distributions, Large Language Models (LLMs) have impressive capabilities, but are prone to outputting falsehoods. The probe’s input is the RM activations when evaluating the LLM’s response. Types of Probes and However, they involve spending substantial computational efforts. Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. In this vein, we analyze how Linear Probes (LPs) can be used to provide an estimation on the performance of a compressed More precisely, we propose to train multiple Bayesian linear models, each predicting the output of a layer given the output of the previous one. iuge9t, 11cjq3, wrg, xhzz8, lhsotp, 8nkky, pg8, tuw0e, s6qxl, wiu8tvpt,