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 51–75 of 216 papers

TitleStatusHype
GERE: Generative Evidence Retrieval for Fact VerificationCode1
Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact VerificationCode1
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature PerspectiveCode1
Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-OasisCode1
Large Language Models are few(1)-shot Table ReasonersCode1
Paragraph-based Transformer Pre-training for Multi-Sentence InferenceCode1
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News DetectionCode1
Evidence-based Factual Error CorrectionCode1
ChartCheck: Explainable Fact-Checking over Real-World Chart ImagesCode1
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference OptimizationCode1
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning ExamplesCode1
Evaluating adversarial attacks against multiple fact verification systems—0
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification—0
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering—0
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables—0
Graph Sequential Network for Reasoning over Sequences—0
On the Importance of Delexicalization for Fact Verification—0
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection—0
Global Responses to the COVID-19 Pandemic: A Case Study of Spatiotemporal Evidence Finding and Verification—0
Distilling the Evidence to Augment Fact Verification Models—0
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification—0
GPTs at Factify 2022: Prompt Aided Fact-Verification—0
Growing and Serving Large Open-domain Knowledge Graphs—0
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability—0
Are Large Language Models Good Fact Checkers: A Preliminary Study—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