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.
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:2610.15314v1 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
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
The study examines how large language models, specifically Llama‑3.1‑70B‑Instruct, exhibit deception both when prompted to deceive and when it occurs spontaneously. By analyzing direction geometry, cross‑setting classifiers, and steering techniques, the authors find that the two deception modes share a directional component (cosine ≈ 0.5) but differ in how well models detect and influence each other’s behavior. Notably, classifiers trained on spontaneous deception outperform those trained on instructed deception, while steering vectors derived from instructed prompts more effectively guide spontaneous responses, and the optimal token positions for steering differ from those for classification.
By Josiah Luikham