34 citations · 54 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks
Jiacheng Chen, Tianhao Liang, Sherman Siu +13
We present MEGA-Bench, an evaluation suite that scales multimodal evaluation to over 500 real-world tasks, to address the highly heterogeneous daily use cases of end users. Our obj…
MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark
Xiang Yue, Tianyu Zheng, Yuansheng Ni +10
This paper introduces MMMU-Pro, a robust version of the Massive Multi-discipline Multimodal Understanding and Reasoning (MMMU) benchmark. MMMU-Pro rigorously assesses multimodal mo…
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
Junjie Wang, Yuxiang Zhang, Minghao Liu +19
Recent advancements in large multimodal models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent ch…
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Yubo Wang, Xueguang Ma, Ge Zhang +14
In the age of large-scale language models, benchmarks like the Massive Multitask Language Understanding (MMLU) have been pivotal in pushing the boundaries of what AI can achieve in…