CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts
Fuente:
arXiv
Saved in:
| Main Authors: | Bharti, Shubham, Cheng, Shiyun, Rho, Jihyun, Zhang, Jianrui, Cai, Mu, Lee, Yong Jae, Rau, Martina, Zhu, Xiaojin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
by: Zhang, Jianrui, et al.
Published: (2024)
by: Zhang, Jianrui, et al.
Published: (2024)
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples
by: Zhang, Jianrui, et al.
Published: (2024)
by: Zhang, Jianrui, et al.
Published: (2024)
VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
by: Zou, Bocheng, et al.
Published: (2024)
by: Zou, Bocheng, et al.
Published: (2024)
Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts
by: Kim, Seon Gyeom, et al.
Published: (2025)
by: Kim, Seon Gyeom, et al.
Published: (2025)
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
by: Chen, Zixin, et al.
Published: (2025)
by: Chen, Zixin, et al.
Published: (2025)
The Perils of Chart Deception: How Misleading Visualizations Affect Vision-Language Models
by: Mahbub, Ridwan, et al.
Published: (2025)
by: Mahbub, Ridwan, et al.
Published: (2025)
How Good (Or Bad) Are LLMs at Detecting Misleading Visualizations?
by: Lo, Leo Yu-Ho, et al.
Published: (2024)
by: Lo, Leo Yu-Ho, et al.
Published: (2024)
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
by: Wei, Jingxuan, et al.
Published: (2025)
by: Wei, Jingxuan, et al.
Published: (2025)
Making AI Agents Evaluate Misleading Charts without Nudging
by: Panda, Swaroop
Published: (2026)
by: Panda, Swaroop
Published: (2026)
How Multimodal LLMs Solve Image Tasks: A Lens on Visual Grounding, Task Reasoning, and Answer Decoding
by: Yu, Zhuoran, et al.
Published: (2025)
by: Yu, Zhuoran, et al.
Published: (2025)
Mind the Motions: Benchmarking Theory-of-Mind in Everyday Body Language
by: Lee, Seungbeen, et al.
Published: (2025)
by: Lee, Seungbeen, et al.
Published: (2025)
Can GPT-4 Models Detect Misleading Visualizations?
by: Alexander, Jason, et al.
Published: (2024)
by: Alexander, Jason, et al.
Published: (2024)
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
by: Chen, Huiyi, et al.
Published: (2025)
by: Chen, Huiyi, et al.
Published: (2025)
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
by: Cai, Mu, et al.
Published: (2024)
by: Cai, Mu, et al.
Published: (2024)
ChartBench: A Benchmark for Complex Visual Reasoning in Charts
by: Xu, Zhengzhuo, et al.
Published: (2023)
by: Xu, Zhengzhuo, et al.
Published: (2023)
Matryoshka Multimodal Models
by: Cai, Mu, et al.
Published: (2024)
by: Cai, Mu, et al.
Published: (2024)
MultiChartQA: Benchmarking Vision-Language Models on Multi-Chart Problems
by: Zhu, Zifeng, et al.
Published: (2024)
by: Zhu, Zifeng, et al.
Published: (2024)
CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
by: Wang, Zirui, et al.
Published: (2024)
by: Wang, Zirui, et al.
Published: (2024)
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
by: Cai, Mu, et al.
Published: (2024)
by: Cai, Mu, et al.
Published: (2024)
Invisible Saboteurs: Sycophantic LLMs Mislead Novices in Problem-Solving Tasks
by: Bo, Jessica Y., et al.
Published: (2025)
by: Bo, Jessica Y., et al.
Published: (2025)
PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter
by: Paudel, Pujan, et al.
Published: (2024)
by: Paudel, Pujan, et al.
Published: (2024)
Distill Visual Chart Reasoning Ability from LLMs to MLLMs
by: He, Wei, et al.
Published: (2024)
by: He, Wei, et al.
Published: (2024)
Rethinking Theory of Mind Benchmarks for LLMs: Towards A User-Centered Perspective
by: Wang, Qiaosi, et al.
Published: (2025)
by: Wang, Qiaosi, et al.
Published: (2025)
MoLT: Mixture of Layer-Wise Tokens for Efficient Audio-Visual Learning
by: Rho, Kyeongha, et al.
Published: (2025)
by: Rho, Kyeongha, et al.
Published: (2025)
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
by: Shang, Yuzhang, et al.
Published: (2024)
by: Shang, Yuzhang, et al.
Published: (2024)
RuozhiBench: Evaluating LLMs with Logical Fallacies and Misleading Premises
by: Zhai, Zenan, et al.
Published: (2025)
by: Zhai, Zenan, et al.
Published: (2025)
Misleading through Inconsistency: A Benchmark for Political Inconsistencies Detection
by: Sagimbayeva, Nursulu, et al.
Published: (2025)
by: Sagimbayeva, Nursulu, et al.
Published: (2025)
ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of Mind
by: Shinoda, Kazutoshi, et al.
Published: (2025)
by: Shinoda, Kazutoshi, et al.
Published: (2025)
Assessing LLMs in Art Contexts: Critique Generation and Theory of Mind Evaluation
by: Arita, Takaya, et al.
Published: (2025)
by: Arita, Takaya, et al.
Published: (2025)
Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs
by: Sinha, Rohit, et al.
Published: (2026)
by: Sinha, Rohit, et al.
Published: (2026)
InterChart: Benchmarking Visual Reasoning Across Decomposed and Distributed Chart Information
by: Iyengar, Anirudh Iyengar Kaniyar Narayana, et al.
Published: (2025)
by: Iyengar, Anirudh Iyengar Kaniyar Narayana, et al.
Published: (2025)
Exploring the Potential of the Large Language Models (LLMs) in Identifying Misleading News Headlines
by: Rony, Md Main Uddin, et al.
Published: (2024)
by: Rony, Md Main Uddin, et al.
Published: (2024)
How Do LLMs See Charts? A Comparative Study on High-Level Visualization Comprehension in Humans and LLMs
by: Jeon, Hyotaek, et al.
Published: (2026)
by: Jeon, Hyotaek, et al.
Published: (2026)
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts
by: Cai, Mu, et al.
Published: (2023)
by: Cai, Mu, et al.
Published: (2023)
Navigating the Mirage: A Dual-Path Agentic Framework for Robust Misleading Chart Question Answering
by: Zhang, Yanjie, et al.
Published: (2026)
by: Zhang, Yanjie, et al.
Published: (2026)
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
by: Lupu, Andrei, et al.
Published: (2025)
by: Lupu, Andrei, et al.
Published: (2025)
MOMENTS: A Comprehensive Multimodal Benchmark for Theory of Mind
by: Villa-Cueva, Emilio, et al.
Published: (2025)
by: Villa-Cueva, Emilio, et al.
Published: (2025)
When Personalization Misleads: Understanding and Mitigating Hallucinations in Personalized LLMs
by: Sun, Zhongxiang, et al.
Published: (2026)
by: Sun, Zhongxiang, et al.
Published: (2026)
Benchmarking and Analyzing Generative Data for Visual Recognition
by: Li, Bo, et al.
Published: (2023)
by: Li, Bo, et al.
Published: (2023)
ChartLens: Fine-grained Visual Attribution in Charts
by: Suri, Manan, et al.
Published: (2025)
by: Suri, Manan, et al.
Published: (2025)
Similar Items
-
Vinoground: Scrutinizing LMMs over Dense Temporal Reasoning with Short Videos
by: Zhang, Jianrui, et al.
Published: (2024) -
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples
by: Zhang, Jianrui, et al.
Published: (2024) -
VGBench: Evaluating Large Language Models on Vector Graphics Understanding and Generation
by: Zou, Bocheng, et al.
Published: (2024) -
Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts
by: Kim, Seon Gyeom, et al.
Published: (2025) -
Unmasking Deceptive Visuals: Benchmarking Multimodal Large Language Models on Misleading Chart Question Answering
by: Chen, Zixin, et al.
Published: (2025)