VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models

arXiv:2411.14832 · cs.CV, cs.AI, cs.CL, cs.LG · Submitted 2024-11-22 · Read on arXiv

cs.CV, cs.AI, cs.CL, cs.LG

Submitted: 2024-11-22

Updated: 2024-11-22

Journal ref: IEEE Access, vol. 13, pp. 21788-21810, 2025

DOI: 10.1109/ACCESS.2025.3535837

Project page: https://camilochs.github.io/visgraphvar-website

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 73× in this paper · explained in 2 episodes
• Vision-language models — 12× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Foundation Model — 3× in this paper · explained in 3 episodes
• Hallucination — 3× in this paper · explained in 4 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Contrastive Language Image Pretraining — 1× in this paper
• Core — 1× in this paper · explained in 2 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Vision-Language Models Can Self-Improve Reasoning via Reflection
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Benchmarking and Improving Detail Image Caption
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• The False Promise of Imitating Proprietary LLMs
• Towards Reasoning in Large Language Models: A Survey
• Are Large Vision Language Models up to the Challenge of Chart Comprehension and Reasoning? An Extensive Investigation into the Capabilities and Limitations of LVLMs
• Large Language Models are Zero-Shot Reasoners
• Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
• VisualBERT: A Simple and Performant Baseline for Vision and Language
• Evaluating Object Hallucination in Large Vision-Language Models
• Graph Matching Networks for Learning the Similarity of Graph Structured Objects
• VisionGraph: Leveraging Large Multimodal Models for Graph Theory Problems in Visual Context
• A Survey on Hallucination in Large Vision-Language Models
• ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
• Self-Refine: Iterative Refinement with Self-Feedback
• Inverse Scaling: When Bigger Isn't Better
• GPT-4 Technical Report
• Learning Transferable Visual Models From Natural Language Supervision
• Self-Reflection in LLM Agents: Effects on Problem-Solving Performance

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