Imagen de Portada

How LLMs Fail to Support Fact-Checking

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Proma, Adiba Mahbub, Pate, Neeley, Druckman, James, Ghoshal, Gourab, He, Hangfeng, Hoque, Ehsan
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Can LLMs Emulate Human Belief Dynamics?
por: Proma, Adiba Mahbub, et al.
Publicado: (2026)

Replicating Human Motivated Reasoning Studies with LLMs
por: Pate, Neeley, et al.
Publicado: (2026)

Personalized Large Language Models Can Increase the Belief Accuracy of Social Networks
por: Proma, Adiba Mahbub, et al.
Publicado: (2025)

Exploring the Role of Randomization on Belief Rigidity in Online Social Networks
por: Proma, Adiba Mahbub, et al.
Publicado: (2024)

Evidence-Grounded Multimodal Misinformation Detection with Attention-Based GNNs
por: Duwal, Sharad, et al.
Publicado: (2025)

Unveiling Divergent Inductive Biases of LLMs on Temporal Data
por: Kishore, Sindhu, et al.
Publicado: (2024)

MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
por: Tang, Liyan, et al.
Publicado: (2024)

InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs
por: Bai, Yuzhuo, et al.
Publicado: (2026)

Can LLMs Automate Fact-Checking Article Writing?
por: Sahnan, Dhruv, et al.
Publicado: (2025)

MERIT: Modular Framework for Multimodal Misinformation Detection with Web-Grounded Reasoning
por: Shopnil, Mir Nafis Sharear, et al.
Publicado: (2025)

Multimodal Large Language Models to Support Real-World Fact-Checking
por: Geng, Jiahui, et al.
Publicado: (2024)

FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs
por: Sawczyn, Albert, et al.
Publicado: (2025)

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts
por: He, Jiayi, et al.
Publicado: (2025)

HAL: Inducing Human-likeness in LLMs with Alignment
por: Hasan, Masum, et al.
Publicado: (2026)

CANDY: Benchmarking LLMs' Limitations and Assistive Potential in Chinese Misinformation Fact-Checking
por: Guo, Ruiling, et al.
Publicado: (2025)

The Role of Model Confidence on Bias Effects in Measured Uncertainties for Vision-Language Models
por: Liu, Xinyi, et al.
Publicado: (2025)

SocREval: Large Language Models with the Socratic Method for Reference-Free Reasoning Evaluation
por: He, Hangfeng, et al.
Publicado: (2023)

On the Role of Model Prior in Real-World Inductive Reasoning
por: Liu, Zhuo, et al.
Publicado: (2024)

An Empirical Analysis on Large Language Models in Debate Evaluation
por: Liu, Xinyi, et al.
Publicado: (2024)

Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs
por: Singhal, Ronit, et al.
Publicado: (2024)

ClaimCheck: Real-Time Fact-Checking with Small Language Models
por: Putta, Akshith Reddy, et al.
Publicado: (2025)

Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models
por: Rahman, Subhey Sadi, et al.
Publicado: (2025)

Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
por: Sahitaj, Premtim, et al.
Publicado: (2025)

A Law of Next-Token Prediction in Large Language Models
por: He, Hangfeng, et al.
Publicado: (2024)

Improving Reliability and Explainability of Medical Question Answering through Atomic Fact Checking in Retrieval-Augmented LLMs
por: Vladika, Juraj, et al.
Publicado: (2025)

AI Can Enhance Creativity in Social Networks
por: Baten, Raiyan Abdul, et al.
Publicado: (2024)

Application and Optimization of Large Models Based on Prompt Tuning for Fact-Check-Worthiness Estimation
por: Yu, Yinglong, et al.
Publicado: (2025)

Evaluating Evidence Attribution in Generated Fact Checking Explanations
por: Xing, Rui, et al.
Publicado: (2024)

Uncertainty-Aware Web-Conditioned Scientific Fact-Checking
por: Vinod, Ashwin, et al.
Publicado: (2026)

Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
por: Roy, Amartya, et al.
Publicado: (2026)

Believe It or Not: How Deeply do LLMs Believe Implanted Facts?
por: Slocum, Stewart, et al.
Publicado: (2025)

OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
por: Iqbal, Hasan, et al.
Publicado: (2024)

CORRECT: Context- and Reference-Augmented Reasoning and Prompting for Fact-Checking
por: Zhang, Delvin Ce, et al.
Publicado: (2025)

COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations
por: Xing, Rui, et al.
Publicado: (2025)

From Chaos to Clarity: Claim Normalization to Empower Fact-Checking
por: Sundriyal, Megha, et al.
Publicado: (2023)

Leveraging LLM Parametric Knowledge for Fact Checking without Retrieval
por: Vazhentsev, Artem, et al.
Publicado: (2026)

Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
por: Gong, Haisong, et al.
Publicado: (2024)

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
por: Yang, Shuo, et al.
Publicado: (2025)

Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
por: Warren, Greta, et al.
Publicado: (2025)

Mitigating Hallucinations in Multimodal Spatial Relations through Constraint-Aware Prompting
por: Wu, Jiarui, et al.
Publicado: (2025)