mlsanityData to 14 Oct 2026

ArtMentor: AI-Assisted Evaluation of Artworks to Explore Multimodal Large Language Models Capabilities

arXiv 2502.13832 ↗First posted 19 Feb 2025cs.HCcited by 9

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

Can Multimodal Large Language Models (MLLMs), with capabilities in perception, recognition, understanding, and reasoning, function as independent assistants in art evaluation dialogues? Current MLLM evaluation methods, which rely on subjective human scoring or costly interviews, lack comprehensive coverage of various scenarios. This paper proposes a process-oriented Human-Computer Interaction (HCI) space design to facilitate more accurate MLLM assessment and development. This approach aids teachers in efficient art evaluation while also recording interactions for MLLM capability assessment. We introduce ArtMentor, a comprehensive space that integrates a dataset and three systems to optimize MLLM evaluation. The dataset consists of 380 sessions conducted by five art teachers across nine critical dimensions. The modular system includes agents for entity recognition, review generation, and suggestion generation, enabling iterative upgrades. Machine learning and natural language processing techniques ensure the reliability of evaluations. The results confirm GPT-4o's effectiveness in assisting teachers in art evaluation dialogues. Our contributions are available at https://artmentor.github.io/.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 22 of its 76 references that are in the map. First posted on arXiv 19 Feb 2025.

Builds on

22 of 76 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond24 Aug

Other references · 21

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
  3. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  4. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,876
  5. 2023MMBench: Is Your Multi-modal Model an All-around Player?cited by 2,141
  6. 2015Microsoft COCO Captions: Data Collection and Evaluation Servercited by 1,462
  7. 2023LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Daycited by 1,305
  8. 2023MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilitiescited by 1,239
  9. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 811
  10. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 333
  11. 2024MM-LLMs: Recent Advances in MultiModal Large Language Modelscited by 278
  12. 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 249
9 more
  1. 2023Osprey: Pixel Understanding with Visual Instruction Tuningcited by 159
  2. 2024Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Wantcited by 101
  3. 2024GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AIcited by 96
  4. 2024A Design Space for Intelligent and Interactive Writing Assistantscited by 76
  5. 2021Understanding and Creating Art with AI: Review and Outlookcited by 60
  6. 2023TouchStone: Evaluating Vision-Language Models by Language Modelscited by 55
  7. 2021Improving Object Detection in Art Images Using Only Style Transfercited by 15
  8. 2020Developing and Operating Artificial Intelligence Models in Trustworthy Autonomous Systemscited by 10
  9. 2021Two-Stage Clustering of Human Preferences for Action Prediction in Assembly Taskscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.

  1. 2026Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understandingcited by 7
  2. 2026The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotationcited by 2
  3. 2026HanMoVLM: Large Vision-Language Models for Professional Artistic Painting Evaluationcited by 1
  4. 2025LTD-Bench: Evaluating Large Language Models by Letting Them Drawcited by 0
  5. 2025KidsArtBench: Multi-Dimensional Children's Art Evaluation with Attribute-Aware MLLMscited by 0
  6. 2026Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contextscited by 0
  7. 2026MIRAGE: A Micro-Interaction Relational Architecture for Grounded Exploration in Multi-Figure Artworkscited by 0
  8. 2026From Style Replication to Style Exploration: Enabling Art Style Exploration with Analyze-Experiment-Resituate Frameworkcited by 0
  9. 2026MOONWALK: Mediating Operations with Intent-Evidence-Action Alignment Across Junior-Supervisor Review Workflows in Animation/VFX Pre-Productioncited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024LLaVA-Docent: Instruction Tuning with Multimodal Large Language Model to Support Art Appreciation Education
  2. 2026EduArt: An educational-level benchmark for evaluating art history knowledge in large language models
  3. 2023MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria
  4. 2026MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
  5. 2023MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
  6. 2025Creation-MMBench: Assessing Context-Aware Creative Intelligence in MLLM
  7. 2025CognArtive: Large Language Models for Automating Art Analysis and Decoding Aesthetic Elements
  8. 2026How LLMs See Creativity: Zero-Shot Scoring of Visual Creativity with Interpretable Reasoning