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RowanSu/lvomnibench

LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs LVOmniBench is a new audio-visual understanding evaluation benchmark in long-form audio-video inputs. ๐ŸŒŸ ๐Ÿ”ฅ News 2026.03.19 ๐ŸŒŸ We are very proud to launch LVOmniBench, the pioneering comprehensive evaluation benchmark of OmniLLMs in Long Audio-Video Understanding Evaluation! โœจ LVOmniBench Introduction Recent advancements in omnimodal large language modelsโ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/RowanSu/lvomnibench.

sourceHugging Faceupdated 2mo agoView on Hugging Face
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<h1>LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs</h1>

AVQA Long-Video LVOmniBench Gemini

LVOmniBench is a new audio-visual understanding evaluation benchmark in long-form audio-video inputs. ๐ŸŒŸ

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๐Ÿ”ฅ News

  • โ€”`2026.03.19` ๐ŸŒŸ We are very proud to launch LVOmniBench, the pioneering comprehensive evaluation benchmark of OmniLLMs in Long Audio-Video Understanding Evaluation!

โœจ LVOmniBench Introduction

Recent advancements in omnimodal large language models (OmniLLMs) have significantly improved the comprehension of audio and video inputs. However, current evaluations primarily focus on short audio and video clips ranging from 10 seconds to 5 minutes, failing to reflect the demands of real-world applications, where videos typically run for tens of minutes. To address this critical gap, we introduce LVOmniBench, a new benchmark designed specifically for the cross-modal comprehension of long-form audio and video.

  • โ€”We curated a diverse collection of long videos, with durations ranging from 10 to 90 minutes and an average duration of 2,069s. This duration represents a greater than sixfold increase in temporal scale compared to that of existing benchmarks for audio-visual understanding.
  • โ€”We manually constructed 1,014 high-quality multiple-choice questions, which are explicitly designed to require joint reasoning across the audio and visual modalities, thereby facilitating a more comprehensive evaluation of OmniLLMs.
  • โ€”Each QA is ranked by difficulty level, and long audio-video understanding poses significant challenges for both current proprietary and open source models!

๐Ÿ”ฎ Evaluation

๐Ÿ“ Prompt:

The common prompt used in our evaluation follows this format:

python
prompt_text = (
    f"Question: {question}\n"
    f"Options:\n{options_str}\n\n"
    "Select the best answer from the options above. "
    "Directly provide the letter representing your choice (A/B/C/D) and nothing else. "
    "Do not include the full text of the option, do not provide any explanation."
)

๐Ÿ“ Leaderboard:

If you want to add your results to our LVOmniBench leaderboard, please contact us at taokeda@westlake.edu.cn