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 101–150 of 216 papers

TitleStatusHype
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting EvidenceCode0
Factify 2: A Multimodal Fake News and Satire News DatasetCode1
BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification—0
INO at Factify 2: Structure Coherence based Multi-Modal Fact VerificationCode0
COVID-VTS: Fact Extraction and Verification on Short Video PlatformsCode1
Team Triple-Check at Factify 2: Parameter-Efficient Large Foundation Models with Feature Representations for Multi-Modal Fact VerificationCode1
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution SimilaritiesCode0
Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture—0
Precise Zero-Shot Dense Retrieval without Relevance LabelsCode2
Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training FrameworkCode1
Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification—0
Exploring Faithful Rationale for Multi-hop Fact Verification via Salience-Aware Graph Learning—0
Converge to the Truth: Factual Error Correction via Iterative Constrained EditingCode0
Data-Efficient Autoregressive Document Retrieval for Fact Verification—0
AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERTCode1
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-trainingCode1
Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Models—0
Learning to Navigate Wikipedia by Taking Random Walks—0
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning ExamplesCode1
Large Language Models are few(1)-shot Table ReasonersCode1
ReAct: Synergizing Reasoning and Acting in Language ModelsCode4
SISER: Semantic-Infused Selective Graph Reasoning for Fact Verification—0
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification—0
Table-based Fact Verification with Self-labeled Keypoint Alignment—0
Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification SystemsCode0
Re2G: Retrieve, Rerank, GenerateCode1
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables—0
Automatic True/False Question Generation for Educational Purpose—0
GPTs at Factify 2022: Prompt Aided Fact-Verification—0
Approximate Conditional Coverage & Calibration via Neural Model Approximations—0
ClaimDiff: Comparing and Contrasting Claims on Contentious IssuesCode0
Paragraph-based Transformer Pre-training for Multi-Sentence InferenceCode1
Learning to Generate Programs for Table Fact Verification via Structure-Aware Semantic ParsingCode0
Table-based Fact Verification with Self-adaptive Mixture of ExpertsCode0
GERE: Generative Evidence Retrieval for Fact VerificationCode1
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection—0
Synthetic Disinformation Attacks on Automated Fact Verification SystemsCode1
Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature PerspectiveCode1
Team Yao at Factify 2022: Utilizing Pre-trained Models and Co-attention Networks for Multi-Modal Fact VerificationCode1
CsFEVER and CTKFacts: Acquiring Czech data for fact verificationCode0
Investigating Zero- and Few-shot Generalization in Fact Verification—0
Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks—0
Paragraph-based Transformer Pretraining for Multi-Sentence Inference—0
Logically at Factify 2022: Multimodal Fact Verification—0
Evidentiality-guided Generation for Knowledge-Intensive NLP TasksCode1
Evidence Decomposition Graph Network for Fact Verification—0
Table-based Fact Verification with Self-adaptive Mixture of Experts—0
Global Responses to the COVID-19 Pandemic: A Case Study of Spatiotemporal Evidence Finding and Verification—0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification—0
Uncertainty Calibration for Ensemble-Based Debiasing Methods—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
10T5-baseKILT-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