Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration
On the map: Retrieval-augmented generation → Hallucination detection in RAG
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Hallucination detection in RAG, one of 15 directions in Retrieval-augmented generation. Builds on 2 of its 12 references that are in the map. First posted on arXiv 9 Aug 2026.
Builds on
2 of 12 references.
References · 2
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2022Thinking Fast and Slow in Large Language Modelscited by 137
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Retromorphic Testing with Hierarchical Verification for Hallucination Detection in RAG
- 2025Hidden in Plain Sight: Evaluation of the Deception Detection Capabilities of LLMs in Multimodal Settings
- 2024Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
- 2026Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs
- 2026"Did you lie?" Evaluating Lie Detectors across Model Scale and Belief-Verified Model Organisms
- 2025Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals
- 2025Real-Time Evaluation Models for RAG: Who Detects Hallucinations Best?
- 2026One Probe Won't Catch Them All: Towards Targeted Deception Detection