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Claim Verification

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Showing 51–100 of 134 papers

TitleStatusHype
INO at Factify 2: Structure Coherence based Multi-Modal Fact VerificationCode0
Low-Shot Learning for Fictional Claim VerificationCode0
MAPLE: Micro Analysis of Pairwise Language Evolution for Few-Shot Claim VerificationCode0
Mention Memory: incorporating textual knowledge into Transformers through entity mention attentionCode0
MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-CheckingCode0
Multi-Hop Fact Checking of Political ClaimsCode0
QuanTemp: A real-world open-domain benchmark for fact-checking numerical claimsCode0
Optimizing Decomposition for Optimal Claim VerificationCode0
QMUL-SDS at SCIVER: Step-by-Step Binary Classification for Scientific Claim VerificationCode0
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting EvidenceCode0
Reasoning-CV: Fine-tuning Powerful Reasoning LLMs for Knowledge-Assisted Claim VerificationCode0
Reinforcement Retrieval Leveraging Fine-grained Feedback for Fact Checking News Claims with Black-Box LLMCode0
RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim VerificationCode0
Robust Claim Verification Through Fact DetectionCode0
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine ConflictCode0
Self-Supervised Claim Identification for Automated Fact CheckingCode0
Table-Text Alignment: Explaining Claim Verification Against Tables in Scientific PapersCode0
TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim VerificationCode0
UKP-Athene: Multi-Sentence Textual Entailment for Claim VerificationCode0
Understanding the Impact of Evidence-Aware Sentence Selection for Fact CheckingCode0
Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge GraphCode0
UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and BenchmarkingCode0
WikiContradiction: Detecting Self-Contradiction Articles on WikipediaCode0
WithdrarXiv: A Large-Scale Dataset for Retraction StudyCode0
Simple Compounded-Label Training for Fact Extraction and Verification—0
AMREx: AMR for Explainable Fact Verification—0
Minimal Evidence Group Identification for Claim Verification—0
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking—0
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims—0
STRIVE: Structured Reasoning for Self-Improvement in Claim Verification—0
MuSciClaims: Multimodal Scientific Claim Verification—0
Navigating the Noisy Crowd: Finding Key Information for Claim Verification—0
NoFake at CheckThat! 2021: Fake News Detection Using BERT—0
Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection—0
NSF-SciFy: Mining the NSF Awards Database for Scientific Claims—0
WhatTheWikiFact: Fact-Checking Claims Against Wikipedia—0
Ta'keed: The First Generative Fact-Checking System for Arabic Claims—0
Overview and Insights from the SciVer Shared Task on Scientific Claim Verification—0
AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification—0
Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims—0
Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification—0
Piecing It All Together: Verifying Multi-Hop Multimodal Claims—0
ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources—0
Team UMBC-FEVER : Claim verification using Semantic Lexical Resources—0
Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs—0
Active PETs: Active Data Annotation Prioritisation for Few-Shot Claim Verification with Pattern Exploiting Training—0
Reasoning Over Semantic-Level Graph for Fact Checking—0
CoverBench: A Challenging Benchmark for Complex Claim Verification—0
``Who said it, and Why?'' Provenance for Natural Language Claims—0
Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors—0
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