The paper reviews 25 years of automated verbal deception detection research, analyzing 289 reports and 3,653 models across 97 datasets. It finds a pooled accuracy of 74.4% with high heterogeneity, driven mainly by methodological quality rather than model complexity. Despite the use of embeddings and large language models, predictive performance has not improved, and only a small fraction of studies use verifiable ground truth or independent evaluation data.
The study reviews 25 years of automated verbal deception detection research, analyzing 3,653 models across 97 datasets. Pooled accuracy is 74.4% (95% CI: 71.2%-77.4%) with high heterogeneity, driven more by methodological quality than model complexity. Only a small fraction of studies use verifiable ground truth or independent evaluation, and the accuracy ceiling of 70‑75% appears unlikely to improve under current practices.
By Riccardo Loconte, Jonas Festor, Zane Fatjanova, Mariam Bolkvadze, Bennett Kleinberg
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
By Ali Asad, Stephen Obadinma, Anshul Pattoo, Wenxuan Zhang, Xiaodan Zhu
arXiv:2608. 08881v1 Announce Type: new Abstract: The current work developed seven Retrieval-Augmented Generation (RAG) models based on leading deception theories and compared how deception judgments were made relative to baseline models.
By David M. Markowitz, Timothy R. Levine
arXiv:2610.07967v1 Announce Type: new
Abstract: As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their r...
By Yiming Xu, Hongyue Yu, Beihua Yang, Zihan Chen, Yixin Liu, Zhen Peng, Bin Shi, Bo Dong, Chao Shen, Irwin King, Qinghua Zheng
arXiv:2504.00285v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are effective at deceiving when prompted to do so. Models that demonstrate better performance on reasoning tasks are a...
By Samuel M. Taylor, Benjamin K. Bergen
arXiv:2610.15314v1 Announce Type: cross
Abstract: A deception detector is only as useful as the trust its alarms deserve. Like a polygraph, detectors that read a language model's internal activations...
By Heng-Sheng Chang
Linear probes can identify behaviors in language model activations but often fail on out‑of‑distribution data. This study shows that projecting inputs onto a small set of principal components (PCs) from the training distribution allows probes for Llama‑3.1‑8B‑Instruct to transfer across three deception‑detection datasets, nearly matching probes trained directly on the test data. By scoring PCs with an LLM judge to select those that encode transferable deception directions, the authors close the baseline‑to‑oracle gap by 78% on Insider Trading Report and 25% on Sandbagging, revealing that subspace selection largely determines OOD robustness.
By Daniel Yoo, Adrians Skapars
The paper investigates whether steering sparse autoencoder (SAE) features linked to deontological ethics can reduce deceptive alignment—specifically the compliance gap—in small language models. Using a bag‑of‑words feature elicitation pipeline, the authors found that SAE steering significantly narrowed the compliance gap in Gemma‑3‑27B‑IT but not in LLaMA‑3‑8B‑Instruct. They also identified a new deceptive behavior, termed deceptive defanging, where models degrade harmfulness while maintaining the appearance of compliance.
By J Koorndijk
arXiv:2607. 14791v1 Announce Type: new Abstract: Transcoders have recently emerged as a promising approach for mechanistic interpretability (MI), enabling circuit-level analysis of model behaviour.
By Darius Lim, Nathan Leow, Xin Wei Chia
The paper presents a white‑box deception detection system that uses probes trained on the largest deception dataset (FIBS) and a novel architecture aggregating across layers and tokens. The probes achieve 98.8% AUC on SHADE‑Arena, outperforming a text‑monitoring baseline, and reach up to 99.7% AUC when detecting introspective deception that cannot be inferred from context alone. They also effectively detect deception in open‑weight models on politically sensitive topics and when models are pressured about their beliefs.
By Oskar J. Hollinsworth, Alex F. Spies, Tigist Diriba, Adam Gleave, Chris Cundy
arXiv:2607. 20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem.
By Amr Moustafa, Max Feser, Florian Mai