ToE is a hierarchical claim verification framework using RL-driven multi-source retrieval, evidence evaluation, and tree aggregation that reports 4-24 point gains over baselines especially on poisoned inputs.
Re-search for the truth: Multi-round retrieval-augmented large language models are strong fake news detectors
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
RASR retrieves cross-video semantic evidence and domain-guided MLLM reports, then fuses multi-view features to beat FakeSV/FakeTT baselines by up to 0.93% accuracy.
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.
citing papers explorer
-
ToE: A Hierarchical and Explainable Claim Verification Framework with Dynamic Multi-source Evidence Retrieval and Aggregation
ToE is a hierarchical claim verification framework using RL-driven multi-source retrieval, evidence evaluation, and tree aggregation that reports 4-24 point gains over baselines especially on poisoned inputs.
-
RASR: Retrieval-Augmented Semantic Reasoning for Fake News Video Detection
RASR retrieves cross-video semantic evidence and domain-guided MLLM reports, then fuses multi-view features to beat FakeSV/FakeTT baselines by up to 0.93% accuracy.
-
Retrieval-Augmented Generation with Graphs (GraphRAG)
A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.