arXiv AI
DeceptionAnalyser: A Web-Based AI Tool for Performing Structured Deception Analysis with Argumentation Schemes and LLMs
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arXiv AI
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Decoding Hidden Deception in Reasoning LLMs: Activation Explainers for Deception Auditing
arXiv:2606. 17478v1 Announce Type: cross Abstract: As LLMs acquire stronger reasoning capabilities, deceptive behavior becomes an increasingly serious safety concern.
arXiv AI
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Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations
The paper examines the reliability of lie detection probes for language models when the models adopt anti-factual personas, such as conspiracy theorists. A dataset of 8,916 human-reviewed responses from three LLMs was created, and eight existing probes were evaluated, revealing many fail to flag falsehoods under these personas. The authors also constructed confounder datasets showing that probes often track spurious correlations like instruction compliance, and propose a simple linear probe that performs best on both persona and confounder tests.
arXiv AI
Jun 10
The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge
arXiv:2606. 10296v1 Announce Type: cross Abstract: Multi-agent debate systems are typically evaluated only on whether the final answer is correct, overlooking the quality of the intermediate reasoning that debate is designed to produce.
arXiv AI
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Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art
arXiv:2607. 29066v1 Announce Type: cross Abstract: Deception detection has critical implications for legal proceedings, law enforcement, and online security.
arXiv AI
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ReportLogic: Evaluating Logical Quality in Deep Research Reports
arXiv:2602. 18446v2 Announce Type: replace-cross Abstract: Users increasingly rely on Large Language Models (LLMs) for Deep Research, using them to synthesize diverse sources into structured reports that support understanding and action.
arXiv Computation and Language
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FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool
FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.