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Papers

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Showing 601–625 of 1282 papers

TitleStatusHype
ViWikiFC: Fact-Checking for Vietnamese Wikipedia-Based Textual Knowledge Source—0
LingML: Linguistic-Informed Machine Learning for Enhanced Fake News Detection—0
Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines—0
Quantifying the Capabilities of LLMs across Scale and Precision—0
Detecting Edited Knowledge in Language Models—0
Can a Hallucinating Model help in Reducing Human "Hallucination"?—0
FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking—0
Large Language Model Agent for Fake News Detection—0
Credible, Unreliable or Leaked?: Evidence Verification for Enhanced Automated Fact-checkingCode0
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities—0
Inside the echo chamber: Linguistic underpinnings of misinformation on TwitterCode0
Augmented CARDS: A machine learning approach to identifying triggers of climate change misinformation on Twitter—0
Classifying Human-Generated and AI-Generated Election Claims in Social Media—0
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking—0
RAGAR, Your Falsehood Radar: RAG-Augmented Reasoning for Political Fact-Checking using Multimodal Large Language Models—0
Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection—0
Misinformation Resilient Search Rankings with Webgraph-based InterventionsCode0
Mitigating Cascading Effects in Large Adversarial Graph Environments—0
Rumour Evaluation with Very Large Language ModelsCode0
Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions—0
Auditing health-related recommendations in social media: A Case Study of Abortion on YouTube—0
Pitfalls of Conversational LLMs on News Debiasing—0
Evaluation of an LLM in Identifying Logical Fallacies: A Call for Rigor When Adopting LLMs in HCI Research—0
NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction HelpsCode0
A (More) Realistic Evaluation Setup for Generalisation of Community Models on Malicious Content DetectionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TOKOFOUAverage F189.7—Unverified