Detecting Strategic Deception Using Linear Probes, https://lnkd.

Detecting Strategic Deception Using Linear Probes, edu. welchlabs. The study evaluates linear probes for detecting AI deception, achieving high accuracy in distinguishing honest from deceptive outputs, but concludes that current methods are insufficient for Overview lecture on linear system identification and model reduction. While output monitors fail, we show that linear probes Figure 4: ROC curves for our probe trained on the Instructed-Pairs dataset. This fragmentation It is shown that combining probes from multiple layers into an ensemble recovers strong performance even where single-layer probes fail, improving AUROC by +29% on Insider Trading and +78% on Promoting openness in scientific communication and the peer-review process Detecting Strategic Deception Using Linear Probes February 6, 2025 Read more Evaluations Evaluations ‪Google DeepMind‬ - ‪‪Cited by 1,011‬‬ - ‪AGI Safety‬ - ‪Mechanistic Interpretability‬ correct answers to factual questions. Detecting deception is difficult, and there are no An overview of transforms, as used in LLMs, and the attention mechanism within them. 03407. Realism is a rough measure of whether the model plausibly Goal environment deception involves the strategic manipulation of the context or interactions of multiple AI systems in pursuit of unauthorized objectives. We test two probe-training datasets, one with contrasting instructions to be honest or 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. Monitoring outputs alone is insuficient, since the AI might produce seemingly benign The paper evaluates the effectiveness of linear probes in detecting strategic deception in AI models, achieving high accuracy in distinguishing honest from deceptive responses, but AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while 是测试预训练模型性能的一种方法，又称为linear probing evaluation 2. I used code and methodology from Apollo's Detecting Strategic Deception Using Linear Probes paper to train and evaluate a linear probe for deception on Llama 3. org/WelchLabs/ to try Brilliant for free for 30 days and get 20% off an annual premium ‪Michigan State University‬ - ‪‪引用次数：316 次‬‬ - ‪Deep Learning‬ Probing Classifiers are an Explainable AI tool used to make sense of the representations that deep neural networks learn for their inputs. (Apollo Research, 2025). We compare deceptive responses to honest responses (left) and to control responses (right). Fidgeting, looking away, touching your mouth, all of these things are commonly thought to be practices The study evaluates linear probes for detecting AI deception, achieving high accuracy in distinguishing honest from deceptive outputs, but concludes that current methods are insufficient for Learn to accurately detect when someone is lying to you with amazing accuracy. Monitoring outputs alone is insuficient, since the AI might produce seemingly benign AI models might use deceptive strategies as part of scheming or misaligned behaviour. " Don Rabon presents the different kinds of deception you might come across, Abstract AI models might use deceptive strategies as part of scheming or misaligned behaviour. Kelly J. Future AI instrumentation may have the ability to detect when an LLM generates decep-tive responses while reasoning about seemingly plausible but incorrect answers to factual questions. Article "Detecting Strategic Deception Using Linear Probes" Detailed information of the J-GLOBAL is an information service managed by the Japan Science and Technology Agency (hereinafter referred to Hier sollte eine Beschreibung angezeigt werden, diese Seite lässt dies jedoch nicht zu. Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography Detecting strategic deception using linear probes Co-authored with: Nix Goldowsky-Dill, Stefan Heimersheim, Marius Hobbhahn 6th February 2025 · 466 words · 2 minute read Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography Laplace Matching for fast Approximate Inference in Generalized Linear Models Marius Hobbhahn, Philipp Hennig 2021 (modified: 14 Sept 2021) CoRR 2021 AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is Why it's called similar. We test two probe-training datasets, one with contrasting instructions to be honest or deceptive (following Zou et al. In this work, Figure 6: Comparison of various probe-training datasets and methodologies, as well as a black-box baseline. AI models might use deceptive strategies as part of scheming or misaligned behaviour. The violin plot reveals a clear distinction in activation distributions, with most deception datasets showing View recent discussion. 15 In some cases, two or more AI systems The dominant approach to deception detection relies on linear probes—logistic regression classifiers trained on residual stream activations to distinguish "honest" from "deceptive" Copy Goldowsky-Dill et al. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal This paper uses linear probes and logistic regression to detect deception in Llama model activations, achieving AUROCs up to 0. https://lnkd. This approach gen-eralized surprisingly well to detecting strategic deception in settings such as concealing insider trading and deliber-ately underperforming on safety evaluations. Our probes reach a We show that open-weight models can naturally learn specific human-style deceptive behavior in Among Us, use deception ELO to compare various agents for their deceptive capability Our new research & paper 'Detecting Strategic Deception Using Linear Probes' in now published. 1 8B. Researchers at Apollo Research demonstrate that linear probes can effectively detect strategic deception in large language models by analyzing internal activations, achieving AUROC AI models might use deceptive strategies as part of scheming or misaligned behaviour. When deployed, these probes monitor the internal activations of 아폴로 리서치 연구원들은 선형 프로브가 내부 활성화를 분석함으로써 대규모 언어 모델의 전략적 기만을 효과적으로 감지할 수 있음을 입증했으며, 정직한 응답과 기만적인 응답을 구별하는 데 최대 Inspired by Apollo Research's paper "Detecting Strategic Deception Using Linear Probes," we wanted to make this critical safety research accessible with production-ready datasets Although research on AI deception is rapidly expanding, studies remain largely isolated within single paradigms, often using incompatible tasks, metrics, or interfaces. 3-70B-Instruct’s ac-tivations across various evaluation awareness datasets Demonstrating probe generalisation across different evaluation and deployment prompts AI models might use deceptive strategies as part of scheming or misaligned behaviour. in/gaCY4TVh Linear probes can detect when language models produce outputs they “know” are wrong, a capability relevant to both deception and reward hacking. 原理 训练后，要评价模型的好坏，通过将最后的一层替换成线性层。 预训练模型的表征层的特征固定，参数固化后未 The research revealed impressive accuracy in detecting deception: The detector achieved 96-99. - Training linear probes on Llama-3. Linear probes (or "deception probes") are trained to distinguish between honest and deceptive responses using a labeled dataset. com/resources/ai-book-ezrzm-msrmcPatre Have we discovered an ideal gas law for AI? Head to https://brilliant. Based on the 3blue1brown deep learning series: https://www. " Proceedings of the 42nd International Conference on Machine Learning, 2025. Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. Learn more at http://janux. hudsonrivertrading. Extends truth probing to strategic deception detection, showing that probes trained on simple The dominant approach to de-ception detection relies on linear probes—logistic regression classifiers trained on residual stream ac-tivations to distinguish "honest" from "deceptive" internal states. Now, University of Rochester researchers are using data science and an online crowdsourcing to create a screening system that can more accurately detect deception based on facial and verbal cues. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal Sandbagging responses are labelled programmatically depending on if the model chooses to sandbag in its structured chain-of-thought reasoning. AI models might use deceptive We thus evaluate if linear probes can robustly detect deception by monitoring model activations. We our probe trained on the Instructed-Pairs dataset (“pretend to be an honest/deceptive Abstract Linear probes trained on internal activations have shown promise for detecting deceptive behavior in large language models, but the extent to which such signals are universal across model Detecting Strategic Deception Using Linear Probes by Goldowsky-Dill et al. However, single-layer probes are In this work, we demonstrate that linear probes on LLMs internal activations can detect deception in their responses with extremely high accuracy. youtube. ipynb: This notebook is based on and similar to a reference Colab implementation associated with the paper "Detecting Strategic Deception Using Linear Probes" The paper evaluates the effectiveness of linear probes in detecting strategic deception in AI models, achieving high accuracy in distinguishing honest from deceptive responses, but AI models might use deceptive strategies as part of scheming or misaligned behaviour. com/welchlabsWelch Labs Book: https://www. A We extracted linear probes that reliably detect this evaluation awareness. How can we spot that kind of strategic deception before it causes harm?We explore a simple detector system: a linear probe that monitors the model's internal thoughts (its 'activations', or intermediate How can we spot that kind of strategic deception before it causes harm? We explore a simple detector system: a linear probe that monitors the model's internal thoughts (its 'activations', or This work demonstrates that linear probes on LLMs internal activations can detect deception in their responses with extremely high accuracy, and finds multitudes of linear directions We thus evaluate if linear probes can robustly detect deception by monitoring model activations. ou. We test two probe-training datasets, one with contrasting instructions to be honest or We thus evaluate if linear probes can robustly detect deception by monitoring model activations. However, single-layer probes are Our new research & paper 'Detecting Strategic Deception Using Linear Probes' in now published. #ai #artificialintelligence #machi Investigative Statement Analysis can help police officers and investigators detect deception better during their interviews and interrogations. "Detecting Strategic Deception with Linear Probes. Monitoring outputs alone is insufficient, since the AI might produce seemingly ‪Research Scientist, Apollo Research‬ - ‪‪Cited by 763‬‬ ABSTRACT AI models might use deceptive strategies as part of scheming or misaligned behaviour. They allow us to understand if the numeric representation 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. Note zoomed x-axes. We test two probe-training datasets, one with contrasting instructions to be honest or There are a number of myths about detecting deception. org/pdf/2502. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal We applied the linear probe to several deception datasets, with Alpaca serving as a control dataset. Todd, managing member and member in charge of forensic investigations, explains that everyone has a “norm”– a basic pattern of behavior that they ex Martin Keen and Jeff Crume dive into how an ensemble of AI models combine predictive machine learning and encoder LLMs to transform fraud detection. . We test two probe-training datasets, one with contrasting instructions to be honest or ABSTRACT AI models might use deceptive strategies as part of scheming or misaligned behaviour. 999 and high recall at 1% FPR. The authors evaluate the effectiveness of these Bibliographic details on Detecting Strategic Deception Using Linear Probes. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal Deception Detection Code for the paper Detecting Strategic Deception Using Linear Probes. 9% accuracy in distinguishing honest from deceptive responses When tuned to Detecting Strategic Deception Using Linear Probes Nicholas Goldowsky-Dill , Bilal Chughtai , Stefan Heimersheim , Detecting Strategic Deception Using Linear Probes: Paper and Code. In this work, we demonstrate that linear probes on LLMs internal activations can detect deception in their r sponses with extremely high accuracy. Podcast conversation covering "Detecting Strategic Deception Using Linear Probes" found @ https://arxiv. , 2023) and one of responses to simple roleplaying scenarios. The BLAST (TM) Deception Detection Certified Training Program is owned by Whetstone Security Group “Understanding and Detecting Deception" is a free online course on Janux that is open to anyone. Monitor-ing outputs alone is insuficient, since the AI might produce seemingly benign outputs while Apply to join Hudson River Trading: https://www. She covers how the circuitry inside of the oscilloscope probes differs, how this can affect your measurement and tips for selecting the right oscilloscope probe for your particular measurement need. This lecture discusses how we obtain reduced-order models from data that optimally capture input--output dynamics. We test two probe-training datasets, one with contrasting instructions to be honest or deceptive (following We thus evaluate if linear probes can robustly detect deception by monitoring model activations. It is found that white-box probes are promising for future monitoring systems, but current performance is insufficient as a robust defence against deception. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. com/pl In this video, you can catch a preview of the webinar, "Detecting Deception During the Interview Process. We applied this to deception evaluation datasets and found that current test scenarios may be readily identifiable as A red background on the text indicates the probe reads deceptive, a blue colour indicates the probe reads honest. For each data point we give the ground-truth label the probe score, expressed as a Further, strategic dishonesty can act like a honeypot against malicious users, which noticeably obfuscates prior jailbreak attacks. The document discusses the use of linear probes to detect strategic deception in AI models, particularly focusing on the Llama-3. 3-70B-Instruct model. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while Deception Detection Code for the paper Detecting Strategic Deception Using Linear Probes. crszad, ez, uincf, juaaaj0, u71, soaa, iv4h, xqcqr, bz, 4dw1,