MDIT-Bench: Evaluating the Dual-Implicit Toxicity in Large Multimodal Models
Fuente:
arXiv
Saved in:
| Main Authors: | Jin, Bohan, Qi, Shuhan, Chen, Kehai, Guo, Xinyi, Wang, Xuan |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
by: Guo, Hongcheng, et al.
Published: (2024)
by: Guo, Hongcheng, et al.
Published: (2024)
DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering
by: Wang, Xinyi, et al.
Published: (2025)
by: Wang, Xinyi, et al.
Published: (2025)
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms
by: Chen, Andong, et al.
Published: (2024)
by: Chen, Andong, et al.
Published: (2024)
TaeBench: Improving Quality of Toxic Adversarial Examples
by: Zhu, Xuan, et al.
Published: (2024)
by: Zhu, Xuan, et al.
Published: (2024)
StyleBench: Evaluating thinking styles in Large Language Models
by: Guo, Junyu, et al.
Published: (2025)
by: Guo, Junyu, et al.
Published: (2025)
Realistic Evaluation of Toxicity in Large Language Models
by: Luong, Tinh Son, et al.
Published: (2024)
by: Luong, Tinh Son, et al.
Published: (2024)
Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving
by: Chen, Andong, et al.
Published: (2024)
by: Chen, Andong, et al.
Published: (2024)
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective
by: Wen, Yuchen, et al.
Published: (2024)
by: Wen, Yuchen, et al.
Published: (2024)
Evaluating and Improving Cultural Awareness of Reward Models for LLM Alignment
by: Zhang, Hongbin, et al.
Published: (2025)
by: Zhang, Hongbin, et al.
Published: (2025)
MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues
by: Liu, Zheyuan, et al.
Published: (2026)
by: Liu, Zheyuan, et al.
Published: (2026)
Pragmatic Inference Chain (PIC) Improving LLMs' Reasoning of Authentic Implicit Toxic Language
by: Chen, Xi, et al.
Published: (2025)
by: Chen, Xi, et al.
Published: (2025)
PCA-Bench: Evaluating Multimodal Large Language Models in Perception-Cognition-Action Chain
by: Chen, Liang, et al.
Published: (2024)
by: Chen, Liang, et al.
Published: (2024)
Rethinking Toxicity Evaluation in Large Language Models: A Multi-Label Perspective
by: Kou, Zhiqiang, et al.
Published: (2025)
by: Kou, Zhiqiang, et al.
Published: (2025)
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
by: Liu, Zheyuan, et al.
Published: (2024)
by: Liu, Zheyuan, et al.
Published: (2024)
Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
by: Qi, Xuan, et al.
Published: (2025)
by: Qi, Xuan, et al.
Published: (2025)
Paying More Attention to Source Context: Mitigating Unfaithful Translations from Large Language Model
by: Zhang, Hongbin, et al.
Published: (2024)
by: Zhang, Hongbin, et al.
Published: (2024)
Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation
by: Wang, Xinyi, et al.
Published: (2026)
by: Wang, Xinyi, et al.
Published: (2026)
SO-Bench: A Structural Output Evaluation of Multimodal LLMs
by: Feng, Di, et al.
Published: (2025)
by: Feng, Di, et al.
Published: (2025)
MedCalc-Bench: Evaluating Large Language Models for Medical Calculations
by: Khandekar, Nikhil, et al.
Published: (2024)
by: Khandekar, Nikhil, et al.
Published: (2024)
TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
by: Chu, Zheng, et al.
Published: (2023)
by: Chu, Zheng, et al.
Published: (2023)
Are Large Vision Language Models Good Game Players?
by: Wang, Xinyu, et al.
Published: (2025)
by: Wang, Xinyu, et al.
Published: (2025)
EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large Language Models
by: Hu, He, et al.
Published: (2025)
by: Hu, He, et al.
Published: (2025)
MPCI-Bench: A Benchmark for Multimodal Pairwise Contextual Integrity Evaluation of Language Model Agents
by: Wang, Shouju, et al.
Published: (2026)
by: Wang, Shouju, et al.
Published: (2026)
Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
by: Zhang, Dingkun, et al.
Published: (2025)
by: Zhang, Dingkun, et al.
Published: (2025)
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
by: Wang, Yiqi, et al.
Published: (2024)
by: Wang, Yiqi, et al.
Published: (2024)
EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection
by: Xu, Ancheng, et al.
Published: (2025)
by: Xu, Ancheng, et al.
Published: (2025)
RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation
by: Niu, Tianyi, et al.
Published: (2025)
by: Niu, Tianyi, et al.
Published: (2025)
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique
by: Zeng, Gailun, et al.
Published: (2025)
by: Zeng, Gailun, et al.
Published: (2025)
An Empirical Analysis on Large Language Models in Debate Evaluation
by: Liu, Xinyi, et al.
Published: (2024)
by: Liu, Xinyi, et al.
Published: (2024)
GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse
by: Lin, Hongzhan, et al.
Published: (2024)
by: Lin, Hongzhan, et al.
Published: (2024)
UrbanPlanBench: A Comprehensive Urban Planning Benchmark for Evaluating Large Language Models
by: Zheng, Yu, et al.
Published: (2025)
by: Zheng, Yu, et al.
Published: (2025)
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
by: Sabour, Sahand, et al.
Published: (2024)
by: Sabour, Sahand, et al.
Published: (2024)
Testing and Evaluation of Large Language Models: Correctness, Non-Toxicity, and Fairness
by: Wang, Wenxuan
Published: (2024)
by: Wang, Wenxuan
Published: (2024)
MoralBench: Moral Evaluation of LLMs
by: Ji, Jianchao, et al.
Published: (2024)
by: Ji, Jianchao, et al.
Published: (2024)
IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations
by: Fu, Deqing, et al.
Published: (2024)
by: Fu, Deqing, et al.
Published: (2024)
OphthBench: A Comprehensive Benchmark for Evaluating Large Language Models in Chinese Ophthalmology
by: Zhou, Chengfeng, et al.
Published: (2025)
by: Zhou, Chengfeng, et al.
Published: (2025)
Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
by: Wang, Xinyi, et al.
Published: (2025)
by: Wang, Xinyi, et al.
Published: (2025)
EssayBench: Evaluating Large Language Models in Multi-Genre Chinese Essay Writing
by: Gao, Fan, et al.
Published: (2025)
by: Gao, Fan, et al.
Published: (2025)
InFoBench: Evaluating Instruction Following Ability in Large Language Models
by: Qin, Yiwei, et al.
Published: (2024)
by: Qin, Yiwei, et al.
Published: (2024)
PromptBench: A Unified Library for Evaluation of Large Language Models
by: Zhu, Kaijie, et al.
Published: (2023)
by: Zhu, Kaijie, et al.
Published: (2023)
Similar Items
-
IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web
by: Guo, Hongcheng, et al.
Published: (2024) -
DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering
by: Wang, Xinyi, et al.
Published: (2025) -
DUAL-REFLECT: Enhancing Large Language Models for Reflective Translation through Dual Learning Feedback Mechanisms
by: Chen, Andong, et al.
Published: (2024) -
TaeBench: Improving Quality of Toxic Adversarial Examples
by: Zhu, Xuan, et al.
Published: (2024) -
StyleBench: Evaluating thinking styles in Large Language Models
by: Guo, Junyu, et al.
Published: (2025)