Cover Image

Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Velutharambath, Aswathy, Wührl, Amelie, Klinger, Roman
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

How Entangled is Factuality and Deception in German?
by: Velutharambath, Aswathy, et al.
Published: (2024)

What if Deception Cannot be Detected? A Cross-Linguistic Study on the Limits of Deception Detection from Text
by: Velutharambath, Aswathy, et al.
Published: (2025)

Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit
by: Velutharambath, Aswathy, et al.
Published: (2026)

Self-Adaptive Paraphrasing and Preference Learning for Improved Claim Verifiability
by: Wührl, Amelie, et al.
Published: (2024)

Understanding Fine-grained Distortions in Reports of Scientific Findings
by: Wührl, Amelie, et al.
Published: (2024)

What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification
by: Wührl, Amelie, et al.
Published: (2024)

Which Demographics do LLMs Default to During Annotation?
by: Schäfer, Johannes, et al.
Published: (2024)

Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
by: Chen, Kang, et al.
Published: (2024)

Semantic Deception: When Reasoning Models Can't Compute an Addition
by: de Leeuw, Nathaniël, et al.
Published: (2025)

LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions
by: Xu, Yang, et al.
Published: (2025)

DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents
by: Mukhopadhyay, Snehasis
Published: (2026)

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
by: Newman, Benjamin, et al.
Published: (2025)

An Assessment of Model-On-Model Deception
by: Heitkoetter, Julius, et al.
Published: (2024)

OpenDeception: Learning Deception and Trust in Human-AI Interaction via Multi-Agent Simulation
by: Wu, Yichen, et al.
Published: (2025)

SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection
by: Rani, Anku, et al.
Published: (2023)

Voting-based Multimodal Automatic Deception Detection
by: Touma, Lana, et al.
Published: (2023)

DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios
by: Huang, Yao, et al.
Published: (2025)

Deceptive Patterns of Intelligent and Interactive Writing Assistants
by: Benharrak, Karim, et al.
Published: (2024)

DiFaR: Enhancing Multimodal Misinformation Detection with Diverse, Factual, and Relevant Rationales
by: Wan, Herun, et al.
Published: (2025)

Dynamic Emotion and Personality Profiling for Multimodal Deception Detection
by: Zheng, Li, et al.
Published: (2026)

Controllable Decontextualization of Yes/No Question and Answers into Factual Statements
by: Mo, Lingbo, et al.
Published: (2024)

To Tell The Truth: Language of Deception and Language Models
by: Hazra, Sanchaita, et al.
Published: (2023)

Trust Me, I Can Convince You: The Contextualized Argument Appraisal Framework
by: Greschner, Lynn, et al.
Published: (2025)

FaStfact: Faster, Stronger Long-Form Factuality Evaluations in LLMs
by: Wan, Yingjia, et al.
Published: (2025)

Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
by: Li, Bangzheng, et al.
Published: (2023)

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations
by: Kumar, Sachin
Published: (2026)

The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence
by: Wan, Herun, et al.
Published: (2026)

DECOR: Auditing LLM Deception via Information Manipulation Theory
by: Cai, Linyue, et al.
Published: (2026)

Do Large Language Models Exhibit Spontaneous Rational Deception?
by: Taylor, Samuel M., et al.
Published: (2025)

Can MLLMs Read the Room? A Multimodal Benchmark for Assessing Deception in Multi-Party Social Interactions
by: Kang, Caixin, et al.
Published: (2025)

Probing the Limits of the Lie Detector Approach to LLM Deception
by: Berger, Tom-Felix
Published: (2026)

AgentShield: Deception-based Compromise Detection for Tool-using LLM Agents
by: Rassul, Yassin H., et al.
Published: (2026)

PARL: Prompt-based Agents for Reinforcement Learning
by: Resendiz, Yarik Menchaca, et al.
Published: (2025)

Deception Abilities Emerged in Large Language Models
by: Hagendorff, Thilo
Published: (2023)

Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing
by: Xie, Jiakuan, et al.
Published: (2025)

Deception in LLMs: Self-Preservation and Autonomous Goals in Large Language Models
by: Barkur, Sudarshan Kamath, et al.
Published: (2025)

LLM-based Affective Text Generation Quality Based on Different Quantization Values
by: Resendiz, Yarik Menchaca, et al.
Published: (2025)

XNote: Benchmarking Automated Community Notes Generation for Image-based Contextual Deception
by: Ma, Jin, et al.
Published: (2026)

Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing
by: Troiano, Enrica, et al.
Published: (2024)

Domain-Independent Deception: A New Taxonomy and Linguistic Analysis
by: Verma, Rakesh M., et al.
Published: (2024)