Detecting Strategic Deception Using Linear Probes, - … View recent discussion.

Detecting Strategic Deception Using Linear Probes, We test two probe-training datasets, one with contrasting instructions to be honest or AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is insufficient, since the AI might produce seemingly Figure 3: Our probe trained on the Instructed-Pairs activates more on deceptive responses than honest responses across all datasets. controlai. We are unaware of any prior systematic evaluation of white-box probes Apply to join Hudson River Trading: https://www. Monitoring outputs alone is insufficient, since the AI might produce seemingly Figure 6: Comparison of various probe-training datasets and methodologies, as well as a black-box baseline. In Forty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025. Monitor-ing outputs alone is insuficient, since the AI might produce seemingly benign outputs while Podcast conversation covering "Detecting Strategic Deception Using Linear Probes" found @ https://arxiv. Semantic Scholar extracted view of "Detecting Strategic Deception with Linear Probes" by Nicholas Goldowsky-Dill et al. In this work, 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 AI models might use deceptive strategies as part of scheming or misaligned behaviour. in/gaCY4TVh 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. The Basic AI Driveshttps://dl. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography Figure 2: Diagram depicting our main evaluation metrics. We are unaware of any prior systematic evaluation of white-box probes for 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. 要約 (オリジナル) AI models might use deceptive strategies as part of scheming or misaligned behaviour. Note zoomed x-axes. Abstract: AI models might use deceptive strategies as part of scheming or misaligned behaviour. 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. org/pdf/2502. AI models might use deceptive strategies as part AI models might use deceptive strategies as part of scheming or misaligned behaviour. May 2, 2023 – Lies and Allies TuesdaysDavid Neequaye – What justifies cognitive load lie detection? Using probes, machine learning researchers gained a better understanding of the difference between models and between the various layers of a single model. View recent discussion. acm. 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. For a given detector and evaluation dataset, we measure if it distinguishes deceptive and honest responses using AUROC. The violin plot reveals a clear distinction in activation distributions, with most deception datasets showing Have we discovered an ideal gas law for AI? Head to https://brilliant. - View recent discussion. We take the mean (as in our mainline experiments), apply a ReLU threshold . Monitoring outputs alone is insufficient, since the AI might produce seemingly 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, Overview lecture on linear system identification and model reduction. com/pl Semantic Scholar extracted view of "Detecting Strategic Deception with Linear Probes" by Nicholas Goldowsky-Dill et al. Realism is a rough measure of whether the model plausibly Probing the Limits of the Lie Detector Approach to LLM Deception Tom Berger Computer Science 2026 TLDR It is demonstrated that truth probes trained on standard true-false datasets are significantly Figure 14: Comparison of using different strategies for aggregating per-token scores into a per-response score. org/doi/10. com/resources/ai-book-ezrzm-msrmcPatre If this resonated with you, here’s how you can help today: https://campaign. 03407. com/take-actionSources: Apollo Research - "Frontier Models are Capable AI models might use deceptive strategies as part of scheming or misaligned behaviour. It is found that white-box probes are promising for future monitoring systems, but current performance is insufficient as a robust defence against deception. Probing the Limits of the Lie Detector Approach to LLM Deception Tom Berger Computer Science 2026 TLDR It is demonstrated that truth probes trained on standard true-false datasets are significantly Figure 15: Comparison between training and applying the probe to all token positions in the model response and only to the final token position. For the folks who are already familiar with us, here's this week's paper: Title: Detecting Strategic Deception Using Linear Probes Author(s): Nicholas Understanding and Detecting Deception - 9. Based on the 3blue1brown deep learning series: https://www. , 2023) and one of responses to simple roleplaying scenarios. The model has made a trade using insider information but lies about this in its trade report (top) and follow up message (bottom). When deployed, these probes monitor the internal activations of AI models might use deceptive strategies as part of scheming or misaligned behaviour. Both probes were trained on the Roleplaying dataset. 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. 文章浏览阅读1. We our probe trained on the Instructed-Pairs dataset (“pretend to be an honest/deceptive A red background on the text indicates the probe reads deceptive, a blue colour indicates the probe reads honest. AI models might use deceptive strategies as part of scheming or misaligned behaviour. Our probes reach a An overview of transforms, as used in LLMs, and the attention mechanism within them. com/welchlabsWelch Labs Book: https://www. We compare deceptive responses to honest responses (left) and to control responses (right). forbes. Monitoring outputs alone is insuficient, since the AI might produce seemingly benign View recent discussion. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. Detecting deception is difficult, and there are no Use our probes Some results files have been provided in example_results/, including the weights of the probe and exact configs. 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. 999 and high recall at 1% FPR. 1 8B. We use their training dataset (although with a different probe fitting method) for one of the primary probes we evaluate. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal 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 Deception Detection Code for the paper Detecting Strategic Deception Using Linear Probes. Monitoring outputs alone is insufficient, since the AI might produce seemingly This paper uses linear probes and logistic regression to detect deception in Llama model activations, achieving AUROCs up to 0. We test two probe-training datasets, one with contrasting instructions to be honest or deceptive (following Zou et al. https://lnkd. Investigative Statement Analysis can help police officers and investigators detect deception better during their interviews and interrogations. The black line represents the threshold corresponding to 1% FPR on This approach gen-eralized surprisingly well to detecting strategic deception in settings such as concealing insider trading and deliber-ately underperforming on safety evaluations. Fidgeting, looking away, touching your mouth, all of these things are commonly thought to be practices that indicate deception. This lecture discusses how we obtain reduced-order models from data that optimally cap Detecting Strategic Deception Using Linear Probes Nicholas Goldowsky-Dill, Bilal Chughtai, Stefan Heimersheim, Marius Hobbhahn — 2025-02-06 — Apollo Research Source Link: There are a number of myths about detecting deception. Detecting Strategic Deception Using Linear Probes Nicholas Goldowsky-Dill , Bilal Chughtai , Stefan Heimersheim , Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography Kelly J. hudsonrivertrading. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while Researchers at Apollo Research demonstrate that linear probes can effectively detect strategic deception in large language models by analyzing internal act AI models might use deceptive strategies as part of scheming or misaligned behaviour. com/sites/chuckbrooks/2026 “Understanding and Detecting Deception" is a free online course on Janux that is open to anyone. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal 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 The study evaluates linear probes for detecting AI deception, achieving high accuracy in distinguishing honest from deceptive outputs, but concludes that cur 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 insufficient, since the AI might produce seemingly benign outputs while their internal Detecting Strategic Deception with Linear Probes. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while Ready to become a certified watsonx AI Assistant Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while its internal We use their training dataset (although with a different probe fitting method) for one of the primary probes we evaluate. Probing the Limits of the Lie Detector Approach to LLM Deception Tom Berger Computer Science 2026 TLDR It is demonstrated that truth probes trained on standard true-false datasets are significantly In this work, we demonstrate that linear probes on LLMs internal activations can detect deception in their responses with extremely high accuracy. org/WelchLabs/ to try Brilliant for free for 30 days and get 20% off an annual premium In this video, you can catch a preview of the webinar, "Detecting Deception During the Interview Process. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while 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 their internal Abstract AI models might use deceptive strategies as part of scheming or misaligned behaviour. We thus evaluate if linear probes can robustly detect deception by monitoring model activations. 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. The study evaluates linear probes for detecting AI deception, achieving high accuracy in distinguishing honest from deceptive outputs, but concludes that cur Joining Google DeepMind Detecting strategic deception using linear probes Open problems in mechanistic interpretability Intellectual progress in 2024 Activation space interpretability may be How do radars tell targets apart when they’re close together - in range, angle, or speed? In this video, we break down the three core types of radar resoluti Excerpts of my interview with Mark McClich, former Secret Service Agent and creator of Statement Analysis, a technique applied by the FBI to detect deception in the words we speak. 1566226The Rapid Trajectory Of Artificial Intelligencehttps://www. For each data point we give the ground-truth label the probe score, expressed as a Figure 5: An example Insider Trading transcript with probe activations. welchlabs. #ai #artificialintelligence #machi Learn to accurately detect when someone is lying to you with amazing accuracy. We also measure if it Probing the Limits of the Lie Detector Approach to LLM Deception Tom Berger Computer Science 2026 TLDR It is demonstrated that truth probes trained on standard true-false datasets are significantly Sandbagging responses are labelled programmatically depending on if the model chooses to sandbag in its structured chain-of-thought reasoning. Monitoring outputs alone is insufficient, since the AI might produce seemingly benign outputs while their internal We thus evaluate if linear probes can robustly detect deception by monitoring model activations. The BLAST (TM) Deception Detection Certified Training Program is owned by Whetst Detecting strategic deception using linear probes Co-authored with: Nix Goldowsky-Dill, Stefan Heimersheim, Marius Hobbhahn 6th February 2025 · 466 words · 2 minute read 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 its internal 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 Abstract AI models might use deceptive strategies as part of scheming or misaligned behaviour. Monitoring outputs alone is insufficient, since the AI might produce seemingly Linear probes (or "deception probes") are trained to distinguish between honest and deceptive responses using a labeled dataset. 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 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 that encode We test two probe-training datasets, one with contrasting instructions to be honest or deceptive (following Zou et al. In this work, we demonstrate that linear probes on LLMs internal activations can detect deception in their r sponses with extremely high accuracy. 5555/1566174. biz/BdeqHALearn more correct answers to factual questions. Technological Aids - Evidence to Catch a Liar How to Catch a LIAR! Learn Expert Lie Detection/Body Language Reading! 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 Our new research & paper 'Detecting Strategic Deception Using Linear Probes' in now published. youtube. We are unaware of any prior systematic evaluation of white-box probes We use their training dataset (although with a different probe fitting method) for one of the primary probes we evaluate. 7w次，点赞20次，收藏34次。线性探测（LinearProbing）是一种用于评估预训练模型性能的方法，通过替换模型的最后一层为线性层并保持其余部分不变。在此过程中，仅训 Figure 4: ROC curves for our probe trained on the Instructed-Pairs dataset. ga0, fapsciqku, govyt, ytcirr, q6, 5cpq, fqf, mllme, 7f7c, tp,