Imagen de Portada

Factuality of Large Language Models: A Survey

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wang, Yuxia, Wang, Minghan, Manzoor, Muhammad Arslan, Liu, Fei, Georgiev, Georgi, Das, Rocktim Jyoti, Nakov, Preslav
Formato:	Preprint
Publicado:	2024
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

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

Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs
por: Manzoor, Muhammad Arslan, et al.
Publicado: (2024)

MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media
por: Manzoor, Muhammad Arslan, et al.
Publicado: (2024)

OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs
por: Wang, Yuxia, et al.
Publicado: (2024)

Rethinking STS and NLI in Large Language Models
por: Wang, Yuxia, et al.
Publicado: (2023)

A Survey of Confidence Estimation and Calibration in Large Language Models
por: Geng, Jiahui, et al.
Publicado: (2023)

Adaptive Conformal Prediction for Improving Factuality of Generations by Large Language Models
por: Rubashevskii, Aleksandr, et al.
Publicado: (2026)

MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation
por: Singh, Harsh, et al.
Publicado: (2024)

A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
por: Geng, Jiahui, et al.
Publicado: (2025)

Beyond Size: How Gradients Shape Pruning Decisions in Large Language Models
por: Das, Rocktim Jyoti, et al.
Publicado: (2023)

Adapting Fake News Detection to the Era of Large Language Models
por: Su, Jinyan, et al.
Publicado: (2023)

HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment
por: Mekky, Ali, et al.
Publicado: (2025)

Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts
por: Mujahid, Zain Muhammad, et al.
Publicado: (2025)

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

DP-Fusion: Token-Level Differentially Private Inference for Large Language Models
por: Thareja, Rushil, et al.
Publicado: (2025)

Factuality Challenges in the Era of Large Language Models
por: Augenstein, Isabelle, et al.
Publicado: (2023)

Detecting Propaganda Techniques in Code-Switched Social Media Text
por: Salman, Muhammad Umar, et al.
Publicado: (2023)

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

Multimodality Representation Learning: A Survey on Evolution, Pretraining and Its Applications
por: Manzoor, Muhammad Arslan, et al.
Publicado: (2023)

EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models
por: Das, Rocktim Jyoti, et al.
Publicado: (2024)

From Multiple-Choice to Extractive QA: A Case Study for English and Arabic
por: Lynn, Teresa, et al.
Publicado: (2024)

The CLEF-2026 FinMMEval Lab: Multilingual and Multimodal Evaluation of Financial AI Systems
por: Xie, Zhuohan, et al.
Publicado: (2026)

Between Underthinking and Overthinking: An Empirical Study of Reasoning Length and correctness in LLMs
por: Su, Jinyan, et al.
Publicado: (2025)

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

How Does Prefix Matter in Reasoning Model Tuning?
por: Tomar, Raj Vardhan, et al.
Publicado: (2026)

UnsafeChain: Enhancing Reasoning Model Safety via Hard Cases
por: Tomar, Raj Vardhan, et al.
Publicado: (2025)

Data Management For Training Large Language Models: A Survey
por: Wang, Zige, et al.
Publicado: (2023)

The FACTS Leaderboard: A Comprehensive Benchmark for Large Language Model Factuality
por: Cheng, Aileen, et al.
Publicado: (2025)

Cracking Factual Knowledge: A Comprehensive Analysis of Degenerate Knowledge Neurons in Large Language Models
por: Chen, Yuheng, et al.
Publicado: (2024)

Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
por: Xu, Derong, et al.
Publicado: (2024)

Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models
por: Wu, Jialiang, et al.
Publicado: (2025)

Discourse Graph Guided Document Translation with Large Language Models
por: Pham, Viet-Thanh, et al.
Publicado: (2025)

FLAME: Factuality-Aware Alignment for Large Language Models
por: Lin, Sheng-Chieh, et al.
Publicado: (2024)

Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons
por: Wang, Yifei, et al.
Publicado: (2024)

FIRE: Fact-checking with Iterative Retrieval and Verification
por: Xie, Zhuohan, et al.
Publicado: (2024)

Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
por: Liu, Xiaoze, et al.
Publicado: (2024)

LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models
por: Tran, Hieu, et al.
Publicado: (2024)

A Survey on Model Compression for Large Language Models
por: Zhu, Xunyu, et al.
Publicado: (2023)

Reasoning Factual Knowledge in Structured Data with Large Language Models
por: Huang, Sirui, et al.
Publicado: (2024)

A Survey on Fairness in Large Language Models
por: Li, Yingji, et al.
Publicado: (2023)