Cover Image

AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bao, Han, Huang, Yue, Wang, Yanbo, Ye, Jiayi, Wang, Xiangqi, Chen, Xiuying, Zhao, Yue, Zhou, Tianyi, Elhoseiny, Mohamed, Zhang, Xiangliang
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AdaReasoner: Adaptive Reasoning Enables More Flexible Thinking in Large Language Models
by: Wang, Xiangqi, et al.
Published: (2025)

SPA: Achieving Consensus in LLM Alignment via Self-Priority Optimization
by: Huang, Yue, et al.
Published: (2025)

AutoBencher: Towards Declarative Benchmark Construction
by: Li, Xiang Lisa, et al.
Published: (2024)

Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models
by: Xu, Zixiang, et al.
Published: (2025)

SocialMaze: A Benchmark for Evaluating Social Reasoning in Large Language Models
by: Xu, Zixiang, et al.
Published: (2025)

AutoBench: Automating LLM Evaluation through Reciprocal Peer Assessment
by: Loi, Dario, et al.
Published: (2025)

AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design
by: Qiu, Ruidi, et al.
Published: (2024)

DyFlow: Dynamic Workflow Framework for Agentic Reasoning
by: Wang, Yanbo, et al.
Published: (2025)

Adaptive Distraction: Probing LLM Contextual Robustness with Automated Tree Search
by: Wang, Yanbo, et al.
Published: (2025)

IDEATOR: Jailbreaking and Benchmarking Large Vision-Language Models Using Themselves
by: Wang, Ruofan, et al.
Published: (2024)

1+1>2: Can Large Language Models Serve as Cross-Lingual Knowledge Aggregators?
by: Huang, Yue, et al.
Published: (2024)

StoryGPT-V: Large Language Models as Consistent Story Visualizers
by: Shen, Xiaoqian, et al.
Published: (2023)

Dual Optimal: Make Your LLM Peer-like with Dignity
by: Wang, Xiangqi, et al.
Published: (2026)

PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models
by: Bao, Han, et al.
Published: (2026)

Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
by: Huang, Yue, et al.
Published: (2026)

Causally-Enhanced Reinforcement Policy Optimization
by: Wang, Xiangqi, et al.
Published: (2025)

VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understanding
by: Li, Xiang, et al.
Published: (2024)

Social Science Meets LLMs: How Reliable Are Large Language Models in Social Simulations?
by: Huang, Yue, et al.
Published: (2024)

Can Large Vision-Language Models Correct Semantic Grounding Errors By Themselves?
by: Liao, Yuan-Hong, et al.
Published: (2024)

Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study
by: Zhou, Yujun, et al.
Published: (2025)

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
by: Chou, Jason, et al.
Published: (2025)

Can Large Vision-Language Models Understand Multimodal Sarcasm?
by: Wang, Xinyu, et al.
Published: (2025)

AutoHallusion: Automatic Generation of Hallucination Benchmarks for Vision-Language Models
by: Wu, Xiyang, et al.
Published: (2024)

Time Blindness: Why Video-Language Models Can't See What Humans Can?
by: Upadhyay, Ujjwal, et al.
Published: (2025)

Prioritization First, Principles Second: An Adaptive Interpretation of Helpful, Honest, and Harmless Principles
by: Huang, Yue, et al.
Published: (2025)

MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
by: Wang, Shengkang, et al.
Published: (2024)

PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
by: Chow, Wei, et al.
Published: (2025)

InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows
by: Ataallah, Kirolos, et al.
Published: (2024)

Shaping the Safety Boundaries: Understanding and Defending Against Jailbreaks in Large Language Models
by: Gao, Lang, et al.
Published: (2024)

Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
by: Akujuobi, Uchenna, et al.
Published: (2020)

AutoSurvey: Large Language Models Can Automatically Write Surveys
by: Wang, Yidong, et al.
Published: (2024)

Synthetic Interaction Data for Scalable Personalization in Large Language Models
by: Ma, Yuchen, et al.
Published: (2026)

MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
by: Ying, Kaining, et al.
Published: (2024)

AI Alignment Breaks at the Edge
by: Bao, Han, et al.
Published: (2026)

AutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving
by: Xing, Shuo, et al.
Published: (2024)

How Well Can Vision Language Models See Image Details?
by: Gou, Chenhui, et al.
Published: (2024)

Can Large Language Models Automatically Jailbreak GPT-4V?
by: Wu, Yuanwei, et al.
Published: (2024)

ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation
by: Zhao, Enyu, et al.
Published: (2025)

Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs
by: Tie, Guiyao, et al.
Published: (2025)

Evaluating Large Language Models with Psychometrics
by: Li, Yuan, et al.
Published: (2024)