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20222026
most citedMMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

34 citations · 53 across the 16 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CV2024

VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation

Xuan He, Dongfu Jiang, Ge Zhang +16

The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric…

cs.AI2024★ 2 cited

GenAI Arena: An Open Evaluation Platform for Generative Models

Dongfu Jiang, Max Ku, Tianle Li +4

Generative AI has made remarkable strides to revolutionize fields such as image and video generation. These advancements are driven by innovative algorithms, architecture, and data…

cs.CL2024★ 34 cited

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…

cs.CV2024★ 2 cited

MANTIS: Interleaved Multi-Image Instruction Tuning

Dongfu Jiang, Xuan He, Huaye Zeng +4

Large multimodal models (LMMs) have shown great results in single-image vision language tasks. However, their abilities to solve multi-image visual language tasks is yet to be impr…

cs.CV2024★ 2 cited

AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks

Max Ku, Cong Wei, Weiming Ren +2

In the dynamic field of digital content creation using generative models, state-of-the-art video editing models still do not offer the level of quality and control that users desir…