Papers Claim Verification
“Claim Verification” 태그가 달린 논문 134편 · 필터 해제
SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification
We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context. SciVer consists of 3,000 expert-annotated examples over…
Claim VerificationRAGRetrieval-augmented GenerationTable-Text Alignment: Explaining Claim Verification Against Tables in Scientific Papers
Scientific claim verification against tables typically requires predicting whether a claim is supported or refuted given a table. However, we argue that predicting the final label alone is insufficient: it reveals little…
Claim VerificationCoordinating Search-Informed Reasoning and Reasoning-Guided Search in Claim Verification
Multi-hop claim verification is inherently challenging, requiring multi-step reasoning to construct verification chains while iteratively searching for information to uncover hidden bridging facts. This process is fundam…
Claim VerificationMuSciClaims: Multimodal Scientific Claim Verification
Assessing scientific claims requires identifying, extracting, and reasoning with multimodal data expressed in information-rich figures in scientific literature. Despite the large body of work in scientific QA, figure cap…
ArticlesClaim VerificationDiagnosticMultimodal ReasoningVerify-in-the-Graph: Entity Disambiguation Enhancement for Complex Claim Verification with Interactive Graph Representation
Claim verification is a long-standing and challenging task that demands not only high accuracy but also explainability of the verification process. This task becomes an emerging research issue in the era of large languag…
Claim VerificationEntity DisambiguationFact CheckingClaimPKG: Enhancing Claim Verification via Pseudo-Subgraph Generation with Lightweight Specialized LLM
Integrating knowledge graphs (KGs) to enhance the reasoning capabilities of large language models (LLMs) is an emerging research challenge in claim verification. While KGs provide structured, semantically rich representa…
Claim VerificationKnowledge GraphsEMULATE: A Multi-Agent Framework for Determining the Veracity of Atomic Claims by Emulating Human Actions
Determining the veracity of atomic claims is an imperative component of many recently proposed fact-checking systems. Many approaches tackle this problem by first retrieving evidence by querying a search engine and then …
Claim VerificationFact CheckingLanguage ModelingLanguage Modelling+1UrduFactCheck: An Agentic Fact-Checking Framework for Urdu with Evidence Boosting and Benchmarking
The rapid use of large language models (LLMs) has raised critical concerns regarding the factual reliability of their outputs, especially in low-resource languages such as Urdu. Existing automated fact-checking solutions…
BenchmarkingClaim VerificationFact CheckingQuestion Answering+1Reasoning-CV: Fine-tuning Powerful Reasoning LLMs for Knowledge-Assisted Claim Verification
Claim verification is essential in combating misinformation, and large language models (LLMs) have recently emerged in this area as powerful tools for assessing the veracity of claims using external knowledge. Existing L…
Claim VerificationMisinformationDetecting Manipulated Contents Using Knowledge-Grounded Inference
The detection of manipulated content, a prevalent form of fake news, has been widely studied in recent years. While existing solutions have been proven effective in fact-checking and analyzing fake news based on historic…
Claim VerificationFact CheckingLarge Language ModelRAG+1CRAVE: A Conflicting Reasoning Approach for Explainable Claim Verification Using LLMs
The rapid spread of misinformation, driven by digital media and AI-generated content, has made automatic claim verification essential. Traditional methods, which depend on expert-annotated evidence, are labor-intensive a…
Claim VerificationLogical ReasoningMisinformationSmall Language ModelCoLoTa: A Dataset for Entity-based Commonsense Reasoning over Long-Tail Knowledge
The rise of Large Language Models (LLMs) has redefined the AI landscape, particularly due to their ability to encode factual and commonsense knowledge, and their outstanding performance in tasks requiring reasoning. Desp…
Claim VerificationGraph Question AnsweringQuestion AnsweringBOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking
Program-guided reasoning has shown promise in complex claim fact-checking by decomposing claims into function calls and executing reasoning programs. However, prior work primarily relies on few-shot in-context learning (…
Claim VerificationDiversityFact CheckingIn-Context LearningSciClaims: An End-to-End Generative System for Biomedical Claim Analysis
Validating key claims in scientific literature, particularly in biomedical research, is essential for ensuring accuracy and advancing knowledge. This process is critical in sectors like the pharmaceutical industry, where…
Claim VerificationInformation RetrievalRetrievalOptimizing Decomposition for Optimal Claim Verification
Current research on the \textit{Decompose-Then-Verify} paradigm for evaluating the factuality of long-form text typically treats decomposition and verification in isolation, overlooking their interactions and potential m…
Bilevel OptimizationClaim VerificationLLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corroborating and refuting documents as evidenc…
Claim VerificationRAGRetrievalRetrieval-augmented GenerationNSF-SciFy: Mining the NSF Awards Database for Scientific Claims
We present NSF-SciFy, a large-scale dataset for scientific claim extraction derived from the National Science Foundation (NSF) awards database, comprising over 400K grant abstracts spanning five decades. While previous d…
16kAbstract generationClaim VerificationCLIPPER: Compression enables long-context synthetic data generation
LLM developers are increasingly reliant on synthetic data, but generating high-quality data for complex long-context reasoning tasks remains challenging. We introduce CLIPPER, a compression-based approach for generating …
Claim VerificationSynthetic Data GenerationNon-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection
Transformer-based models dominate NLP tasks like sentiment analysis, machine translation, and claim verification. However, their massive computational demands and lack of interpretability pose challenges for real-world a…
Claim VerificationDependency ParsingGraph ClassificationMachine Translation+2STRIVE: Structured Reasoning for Self-Improvement in Claim Verification
Claim verification is the task of determining whether a claim is supported or refuted by evidence. Self-improvement methods, where reasoning chains are generated and those leading to correct results are selected for trai…
Claim VerificationMathematical Problem-Solving