An empirical study distills a taxonomy of human factual errors from newspaper corrections and shows LLMs achieve only 52% F1 on detection.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 5verdicts
UNVERDICTED 5representative citing papers
ReMMD presents ReMMDBench (500 samples, 2756 images, five languages, five-way veracity) and ReMMD-Agent, which achieves 41.80% accuracy and 39.12% macro-F1 on five-way classification with GPT-5.2 while cutting costs versus prior agents.
PrimeFacts extracts decontextualized premises from fact-check articles, raising evidence retrieval MRR by up to 30% and verdict prediction Macro-F1 by 10-20 points over baselines.
FactNet is a billion-scale multilingual knowledge graph that links 1.7B Wikidata assertions to 3.01B byte-precise evidence spans from 316 Wikipedia editions, accompanied by a leakage-controlled benchmark suite.
SEEK uses adaptive semantic chunking to create complete evidence units and fine-tunes multilingual LLMs with LoRA, achieving up to 20% better macro-F1 on fact-checking datasets compared to baselines.
citing papers explorer
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An Empirical Analysis of Factual Errors in Human-Written Text and its Application
An empirical study distills a taxonomy of human factual errors from newspaper corrections and shows LLMs achieve only 52% F1 on detection.
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ReMMD: Realistic Multilingual Multi-Image Agentic Verification for Multimodal Misinformation Detection
ReMMD presents ReMMDBench (500 samples, 2756 images, five languages, five-way veracity) and ReMMD-Agent, which achieves 41.80% accuracy and 39.12% macro-F1 on five-way classification with GPT-5.2 while cutting costs versus prior agents.
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From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence
PrimeFacts extracts decontextualized premises from fact-check articles, raising evidence retrieval MRR by up to 30% and verdict prediction Macro-F1 by 10-20 points over baselines.
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FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
FactNet is a billion-scale multilingual knowledge graph that links 1.7B Wikidata assertions to 3.01B byte-precise evidence spans from 316 Wikipedia editions, accompanied by a leakage-controlled benchmark suite.
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SEEK: Semantic Evidence Extraction via Adaptive ChunKing for Multilingual Fact-Checking
SEEK uses adaptive semantic chunking to create complete evidence units and fine-tunes multilingual LLMs with LoRA, achieving up to 20% better macro-F1 on fact-checking datasets compared to baselines.