SOTAVerified

Fact Verification

Fact verification, also called "fact checking", is a process of verifying facts in natural text against a database of facts.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 216 papers

TitleStatusHype
Are Large Language Models Good Fact Checkers: A Preliminary Study—0
Stochastic RAG: End-to-End Retrieval-Augmented Generation through Expected Utility Maximization—0
Structural Encoding and Pre-training Matter: Adapting BERT for Table-Based Fact Verification—0
Approximate Conditional Coverage & Calibration via Neural Model Approximations—0
FactLens: Benchmarking Fine-Grained Fact Verification—0
FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering—0
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation—0
Findings of Factify 2: Multimodal Fake News Detection—0
Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition—0
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking—0
Students Who Study Together Learn Better: On the Importance of Collective Knowledge Distillation for Domain Transfer in Fact Verification—0
ChronoFact: Timeline-based Temporal Fact Verification—0
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering—0
FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking—0
Extract and Aggregate: A Novel Domain-Independent Approach to Factual Data Verification—0
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification—0
Global Responses to the COVID-19 Pandemic: A Case Study of Spatiotemporal Evidence Finding and Verification—0
GPTs at Factify 2022: Prompt Aided Fact-Verification—0
Graph Sequential Network for Reasoning over Sequences—0
Growing and Serving Large Open-domain Knowledge Graphs—0
Synthetic News Generation for Fake News Classification—0
An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models—0
Uncertainty Calibration for Ensemble-Based Debiasing Methods—0
Exploring Listwise Evidence Reasoning with T5 for Fact Verification—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
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All datasetsKILT: FEVERFEVERDanFEVER

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Re2GKILT-AC78.53—Unverified
2intersectKILT-AC71.28—Unverified
3WikipediaKILT-AC65.68—Unverified
4KGIKILT-AC64.41—Unverified
5Multitask DPR + BARTKILT-AC63.94—Unverified
6BERT + DPRKILT-AC58.58—Unverified
7RAGKILT-AC53.45—Unverified
8BART + DPRKILT-AC47.68—Unverified
9NSMNKILT-AC41.88—Unverified
10ElefPavKILT-AC0—Unverified
#ModelMetricClaimedVerifiedStatus
1ProoFVer-SBAccuracy79.47—Unverified
2DREAMAccuracy76.85—Unverified
3RoBERTa-Base Joint MSPP FlexibleAccuracy75.36—Unverified
4RoBERTa-Base Joint MSPPAccuracy74.39—Unverified
5KGATAccuracy74.1—Unverified
6RAGAccuracy72.5—Unverified
7GEARAccuracy71.6—Unverified
#ModelMetricClaimedVerifiedStatus
1DanFEVER XLM-RoBERTa LargeF10.9—Unverified