How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions [PDF]
arXiv, 2023 Large language models (LLMs) can "lie", which we define as outputting false statements despite "knowing" the truth in a demonstrable sense. LLMs might "lie", for example, when instructed to output misinformation. Here, we develop a simple lie detector that requires neither access to the LLM's activations (black-box) nor ground-truth knowledge of the ...
Lorenzo Pacchiardi +7 more
arxiv +3 more sources
The Science of Lie Detection by Verbal Cues: What Are the Prospects for Its Practical Applicability?
Frontiers in Psychology, 2022 There is agreement among researchers that no simple verbal cues to deception detectable by humans have been demonstrated. This paper examines the evidence for the most prominent current methods, critically considers the prevailing research strategy ...
Tim Brennen, Svein Magnussen
doaj +2 more sources
Five Reasons Why I Am Skeptical That Indirect or Unconscious Lie Detection Is Superior to Direct Deception Detection [PDF]
Frontiers in Psychology, 2019 The relative advantage of indirect and unconscious lie detection compared to direct detection is examined. Empirical evidence for the superiority of indirect and unconscious lie is unconvincing.
Timothy R. Levine
doaj +2 more sources
Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection [PDF]
Computer Vision and Pattern Recognition, 2020 Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods.
Alexandros Haliassos +3 more
openalex +3 more sources
Digging Further Into the Speech of Liars: Future Research Prospects in Verbal Lie Detection [PDF]
Frontiers in Psychiatry, 2019 The field of verbal lie detection has grown rapidly in the past decade. Derived by the assumption that lies have different content patterns than do truths, research in this area promotes searching for content criteria to detect them.
Galit Nahari, Zvi Nisin
openalex +2 more sources
A cognitive approach to lie detection: A meta‐analysis [PDF]
, 2015 Introduction This article provides a meta-analysis of a new, cognitive approach to (non-)verbal lie detection. This cognitive lie detection approach consists of three techniques: (1) imposing cognitive load, (2) encouraging interviewees to say more, and (
Aldert Vrij +2 more
openalex +2 more sources
Machine Learning-Based Lie Detector Applied to a Novel Annotated Game Dataset [PDF]
Future Internet, 2021 Lie detection is considered a concern for everyone in their day-to-day life, given its impact on human interactions. Thus, people normally pay attention to both what their interlocutors are saying and to their visual appearance, including the face, to ...
Nuria Rodriguez-Diaz +3 more
doaj +2 more sources
Verbal Deception and the Model Statement as a Lie Detection Tool
Frontiers in Psychiatry, 2018 We have been reliably informed by practitioners that police officers and intelligence officers across the world have started to use the Model Statement lie detection technique. In this article we introduce this technique. We describe why it works, report
Aldert Vrij +2 more
doaj +2 more sources
Covert lie detection using keyboard dynamics
Scientific Reports, 2018 Identifying the true identity of a subject in the absence of external verification criteria (documents, DNA, fingerprints, etc.) is an unresolved issue.
Merylin Monaro +6 more
doaj +2 more sources
Accuracy, Confidence, and Experiential Criteria for Lie Detection Through a Videotaped Interview
Frontiers in Psychiatry, 2019 An individual's ability to discriminate lies from truth is far from accurate, and is poorly related to an individual's confidence in his/her detection.
Antonietta Curci +4 more
doaj +2 more sources