5 papers
MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale
Jarvis Guo, Tuney Zheng, Yuelin Bai +7
Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained…
Underestimated Privacy Risks for Minority Populations in Large Language Model Unlearning
Rongzhe Wei, Mufei Li, Mohsen Ghassemi +7
Large Language Models (LLMs) embed sensitive, human-generated data, prompting the need for unlearning methods. Although certified unlearning offers strong privacy guarantees, its r…
VisualWebInstruct: Scaling up Multimodal Instruction Data through Web Search
Yiming Jia, Jiachen Li, Xiang Yue +4
Vision-Language Models have made significant progress on many perception-focused tasks. However, their progress on reasoning-focused tasks remains limited due to the lack of high-q…
MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
Jinjie Ni, Yifan Song, Deepanway Ghosal +10
Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their de…
LIME: Less Is More for MLLM Evaluation
King Zhu, Qianbo Zang, Shian Jia +18
Multimodal Large Language Models (MLLMs) are evaluated on various benchmarks, such as image captioning, visual question answering, and reasoning. However, many of these benchmarks…