Cover Image

Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Chaoya, Jia, Hongrui, Ye, Wei, Dong, Mengfan, Xu, Haiyang, Yan, Ming, Zhang, Ji, Zhang, Shikun
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
by: Jiang, Chaoya, et al.
Published: (2024)

Hallucination Augmented Contrastive Learning for Multimodal Large Language Model
by: Jiang, Chaoya, et al.
Published: (2023)

SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization
by: Jia, Hongrui, et al.
Published: (2024)

Mitigating Visual Context Degradation in Large Multimodal Models: A Training-Free Decoupled Agentic Framework
by: Jia, Hongrui, et al.
Published: (2025)

TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training
by: Jiang, Chaoya, et al.
Published: (2023)

From Blind Spots to Gains: Diagnostic-Driven Iterative Training for Large Multimodal Models
by: Jia, Hongrui, et al.
Published: (2026)

ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
by: Wang, Xingqi, et al.
Published: (2025)

COPA: Efficient Vision-Language Pre-training Through Collaborative Object- and Patch-Text Alignment
by: Jiang, Chaoya, et al.
Published: (2023)

Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
by: Ye, Wei, et al.
Published: (2024)

TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
by: Jiang, Chaoya, et al.
Published: (2023)

VLM-R$^3$: Region Recognition, Reasoning, and Refinement for Enhanced Multimodal Chain-of-Thought
by: Jiang, Chaoya, et al.
Published: (2025)

BUS:Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization
by: Jiang, Chaoya, et al.
Published: (2023)

FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
by: Yu, Zhuohao, et al.
Published: (2024)

Exploiting Pseudo Image Captions for Multimodal Summarization
by: Jiang, Chaoya, et al.
Published: (2023)

EVE: Verifiable Self-Evolution of MLLMs via Executable Visual Transformations
by: Heng, Yongrui, et al.
Published: (2026)

AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs
by: Alansari, Aisha, et al.
Published: (2025)

OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents
by: Jia, Hongrui, et al.
Published: (2025)

MIBench: Evaluating Multimodal Large Language Models over Multiple Images
by: Liu, Haowei, et al.
Published: (2024)

FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models
by: Jing, Liqiang, et al.
Published: (2023)

LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
by: Qiu, Han, et al.
Published: (2024)

HalLoc: Token-level Localization of Hallucinations for Vision Language Models
by: Park, Eunkyu, et al.
Published: (2025)

VidHal: Benchmarking Temporal Hallucinations in Vision LLMs
by: Choong, Wey Yeh, et al.
Published: (2024)

MedHal: An Evaluation Dataset for Medical Hallucination Detection
by: Mehenni, Gaya, et al.
Published: (2025)

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
by: Xing, Shangyu, et al.
Published: (2024)

Enhancing In-Context Learning via Implicit Demonstration Augmentation
by: Zhou, Xiaoling, et al.
Published: (2024)

SHALE: A Scalable Benchmark for Fine-grained Hallucination Evaluation in LVLMs
by: Yan, Bei, et al.
Published: (2025)

A Survey on Evaluating Large Language Models in Code Generation Tasks
by: Chen, Liguo, et al.
Published: (2024)

What Makes "Good" Distractors for Object Hallucination Evaluation in Large Vision-Language Models?
by: Xie, Ming-Kun, et al.
Published: (2025)

Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models
by: Wu, Mingrui, et al.
Published: (2024)

RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models
by: Hu, Xiangkun, et al.
Published: (2024)

MapEval: Towards Unified, Robust and Efficient SLAM Map Evaluation Framework
by: Hu, Xiangcheng, et al.
Published: (2024)

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models
by: Chen, Jiawei, et al.
Published: (2024)

MultiHal: Multilingual Dataset for Knowledge-Graph Grounded Evaluation of LLM Hallucinations
by: Lavrinovics, Ernests, et al.
Published: (2025)

Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
by: Yan, Bei, et al.
Published: (2024)

KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
by: Yu, Zhuohao, et al.
Published: (2024)

A Unified Hallucination Mitigation Framework for Large Vision-Language Models
by: Chang, Yue, et al.
Published: (2024)

BenHalluEval: A Multi-Task Hallucination Evaluation Framework for Large Language Models on Bengali
by: Adib, Shefayat E Shams, et al.
Published: (2026)

Mitigating Multilingual Hallucination in Large Vision-Language Models
by: Qu, Xiaoye, et al.
Published: (2024)

AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
by: Wang, Junyang, et al.
Published: (2023)

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models
by: Zhang, Chengsheng, et al.
Published: (2026)