Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration
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 smal...
| Authors | David M. Markowitz, Timothy R. Levine |
|---|---|
| Primary category | cs.AI |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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