A persistent accuracy ceiling in automated verbal deception detection

Riccardo Loconte; Jonas Festor; Zane Fatjanova; Mariam Bolkvadze; Bennett Kleinberg

cs.CLOct 8, 2026

A persistent accuracy ceiling in automated verbal deception detection

Authors: Riccardo Loconte, Jonas Festor, Zane Fatjanova, Mariam Bolkvadze, Bennett Kleinberg

Organizations: Department of Methodology and Statistics, Tilburg University; Tilburg, Netherlands. · Department of Security and Crime Science, University College London; London, UK.

Abstract

Automated methods have been proposed to overcome the limitations of human verbal deception detection, but evidence remains fragmented across disciplines. We systematically reviewed 25 years of research (289 reports, 6,136 classification models) and meta-analyzed 3,653 models nested within 97 datasets. Pooled accuracy was 74.4% (95% CI: 71.2%-77.4%) with substantial heterogeneity. Accuracy was driven by methodological quality (ground truth, data source, class balance, evaluation procedure) more than by model complexity: the adoption of embeddings and large language models has not translated into improved predictive performance. Only 12.46% of reports used data with verifiable ground-truth, and only 23.96% of models were evaluated on independent data. The pooled accuracy aligns with meta-analyses of manual approaches, suggesting a ceiling of 70-75%, unlikely to be lifted by current research conventions.

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Aug 9, 2026, cs.AIJ/K move · Enter open · S save

Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration

David M. Markowitz, Timothy R. Levine

Department of Communication, Michigan State University, East Lansing, MI 48824 · Department of Communication, University of Oklahoma, Norman, OK 73019

Retrieval-Augmented Generation Deception in Language Models LLM Reliability

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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. Across 700 statements drawn from five published deception datasets, four large language models (gpt-4o, claude-sonnet-4-6, ollama/llama3, deepseek-v4-flash), and two run-types (RAG vs. baseline), a total of 39,200 deception judgments were rendered. Detection accuracies were consistent with typical human accuracies and not statistically different across RAG (54.5%) and baseline models (54.6%). RAG-based models (57.0%) were less truth-biased than baseline models (59.7%), but the effect size was quite small. Theoretical perspective mattered little for accuracy yet mattered substantially for response bias, which ranged from highly lie-biased (the verifiability approach, 32.2%) to highly truth-biased (truth-default theory, 88.1%). Content effects and model effects further moderated the results. Theory-guided AI judgments are unreliable with current parameters, yet they might show promise with additional datasets, model testing, and theory-to-data matching.

Abstract

Explore similar work

Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration

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