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 51–100 of 216 papers

TitleStatusHype
Explain and Predict, and then Predict AgainCode1
Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact VerificationCode1
Paragraph-based Transformer Pre-training for Multi-Sentence InferenceCode1
ProoFVer: Natural Logic Theorem Proving for Fact VerificationCode1
KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language ModelsCode1
ChartCheck: Explainable Fact-Checking over Real-World Chart ImagesCode1
LOREN: Logic-Regularized Reasoning for Interpretable Fact VerificationCode1
Re2G: Retrieve, Rerank, GenerateCode1
Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training FrameworkCode1
EX-FEVER: A Dataset for Multi-hop Explainable Fact VerificationCode1
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake NewsCode1
Evaluating adversarial attacks against multiple fact verification systems—0
Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy—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
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification—0
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling—0
DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection—0
Logically at Factify 2: A Multi-Modal Fact Checking System Based on Evidence Retrieval techniques and Transformer Encoder Architecture—0
Distilling the Evidence to Augment Fact Verification Models—0
Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification—0
Logically at Factify 2022: Multimodal Fact Verification—0
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability—0
Are Large Language Models Good Fact Checkers: A Preliminary Study—0
Dataset of Fake News Detection and Fact Verification: A Survey—0
BreakingBERT@IITK at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables—0
Data-Efficient Autoregressive Document Retrieval for Fact Verification—0
Data and Model Distillation as a Solution for Domain-transferable Fact Verification—0
BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data—0
GLAF: Global-to-Local Aggregation and Fission Network for Semantic Level Fact Verification—0
Large Language Model for Table Processing: A Survey—0
Learning to Navigate Wikipedia by Taking Random Walks—0
LookupForensics: A Large-Scale Multi-Task Dataset for Multi-Phase Image-Based Fact Verification—0
Improving Retrieval Augmented Language Model with Self-Reasoning—0
Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition—0
Improving the fact-checking performance of language models by relying on their entailment ability—0
Findings of Factify 2: Multimodal Fake News Detection—0
FiD-Ex: Improving Sequence-to-Sequence Models for Extractive Rationale Generation—0
Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models—0
Investigating Zero- and Few-shot Generalization in Fact Verification—0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification—0
FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking—0
BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification—0
Hypothetical Documents or Knowledge Leakage? Rethinking LLM-based Query Expansion—0
Constrained Fact Verification for FEVER—0
Approximate Conditional Coverage & Calibration via Neural Model Approximations—0
How Robust are Fact Checking Systems on Colloquial Claims?—0
FactLens: Benchmarking Fine-Grained Fact Verification—0
Global Responses to the COVID-19 Pandemic: A Case Study of Spatiotemporal Evidence Finding and Verification—0
Consistent Multi-Granular Rationale Extraction for Explainable Multi-hop Fact Verification—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