Datasets:
language:
- en
license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
task_categories:
- video-text-to-text
tags:
- video-understanding
- large-video-language-models
- lvlm
- positional-bias
- benchmark
- evaluation
extra_gated_prompt: >-
You acknowledge and understand that: This dataset is provided solely for
academic research purposes. It is not intended for commercial use or any other
non-research activities. All copyrights, trademarks, and other intellectual
property rights related to the videos in the dataset remain the exclusive
property of their respective owners.
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: question_id
dtype: string
- name: question
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- name: gt_answer
dtype: string
- name: video_name
dtype: string
- name: question_type
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- name: answer_number
dtype: int64
- name: candidates
sequence: string
- name: video_len
dtype: float64
- name: video_category
dtype: string
- name: human_verification
dtype: bool
splits:
- name: train
num_bytes: 490082
num_examples: 1177
download_size: 224148
dataset_size: 490082
Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models
📜 License
Video-LevelGauge is under the CC-BY-NC-SA-4.0 license. It is derived from several previously published datasets (VideoMME, MLVU, VisDrone, UCF-Crime, and Ego4D). Please note that the original datasets may have their own licenses. Users must comply with the licenses of the original datasets when using this derived dataset.
⚠️ If you access and use our dataset, you must understand and agree: Video-LevelGauge is only used for academic research. Commercial use in any form is prohibited. The user assumes all effects arising from any other use and dissemination.
We do not own the copyright of any raw video files and the copyright of all videos belongs to the video owners. Currently, we provide video access to researchers under the condition of acknowledging the above license. For the video data used, we respect and acknowledge any copyrights of the video authors. If there is any infringement in our dataset, please email [email protected] and we will remove it immediately.
🏠 Introduction
🔔 Large Video Language Models (LVLMs) suffer from positional bias, characterized by uneven comprehension of identical content presented at different contextual positions.
👀 Video-LevelGauge Overview
Video-LevelGauge is explicitly designed to investigate contextual positional bias in video understanding. We introduce a standardized probe and customized context design paradigm, where carefully designed probe segments are inserted at varying positions within customized contextual contents. By comparing model responses to identical probes at different insertion points, we assess positional bias in video comprehension. It supports flexible control over context length, probe position, and context composition to evaluate positional biases in various real-world scenarios, such as multi-video understanding, long video comprehension and multi-modal interleaved inputs. Video-LevelGauge encompasses six categories of structured video understanding tasks (e.g., action reasoning), along with an open-ended descriptive task. It includes 438 manually collected multi-type videos, 1,177 multiple-choice question answering (MCQA) items, and 120 open-ended instructed descriptive problems paired with annotations.
🔍 Dataset
The annotation file and the raw videos are readily accessible via this HF Link 🤗. Note that this dataset is for research purposes only and you must strictly comply with the above License.
🚀 Sample Usage
To quickly get started with running inference and evaluating models on Video-LevelGauge, follow these steps. For more detailed instructions and examples, please refer to the GitHub repository.
✨ Clone and Prepare Dataset
First, please clone this repository and download our dataset into ./LevelGauge, organizing it as follows:
Video-LevelGauge
├── asset
├── evaluation
├── LevelGauge
│ ├── json
│ └── videos
├── metric
├── output
├── preprocess
✨ Running Inference
We take three models as examples to demonstrate how to use our benchmark for positional bias evaluation:
- InternVL3 – inference with
transformers. - MiMo-VL – inference with
vLLM API, using video input.
(If you plan to call the commercial API for testing, this is a good reference.) - GLM-4.5V – inference with
vLLM API, using multi-image input.
For InternVL3, please follow the official project to set up the environment. Run inference as follow:
bash ./evaluation/transformer/eval_intervl3.sh
The accuracy at each position will be computed and saved to acc_dir: ./output/internvl_acc.
For MiMo-VL, please first follow the official project to deploy the model with vLLM. Run inference as follow:
bash ./evaluation/vllm/eval_mimovl.sh
The accuracy at each position will be computed and saved to acc_dir: ./output/mimovl_acc.
For GLM-4.5V, please first follow the official project to deploy the model with vLLM. Run inference as follow:
bash ./evaluation/vllm/eval_glm45v.sh
The accuracy at each position will be computed and saved to acc_dir: ./output/glm45v_acc.
📌 In addition, we provide preprocessing scripts, including:
frame extraction and concatenating probe and background videos into a single video. See the ./preprocess folder.
You can choose the input method based on your model. Concatenating probe and background videos into a single video is recommended as it is applicable to all models.
📌 For precise investigation, in our paper, we evaluate models on the full set of our 1,177 samples, which requires tens of thousands of inferences across 10 positions. We provide a subset of 300 samples for quick testing 🚀.
✨ Metric Calculation
Once positional accuracies are saved to acc_dir, you can compute all metrics in one command 😄, including Pran, Pvar, Pmean, MR, etc. We use the provided files in ./output/example_acc as an example:
python ./metric/metric.py --acc_dir ./output/example_acc
Finally, we provide a script for visualizing positional bias. See bias_plot.py for details.
🔮 Evaluation PipLine
Please refer to our 🍎 project and 📖arXiv Paper for more details.
📈 Experimental Results
📍Performance of state-of-the-art LVLMs on Video-LevelGauge.
Gemini 2.5 Pro exhibits the least positional bias, followed by GLM-4.5V, GPT-4o-latest, Doubao-Seed-1.6, and other models.
📍Evaluation results of Stat-of-the-art LVLMs.
We conduct a comprehensive investigation of 27 LVLMs using Video-LevelGauge, including 6 commercial models, i.e., Gemini 2.5 Pro and QVQ-Max; 21 open-source LVLMs covering unified models like InternVL3, long video models like Video-XL2, specific optimized models like VideoRefer, multi-modal reasoning models like GLM-4.5V, and two-stage methods like LLoVi.
📍Effect of Context Length on Positional Bias.
Positional bias is prevalent across various context lengths and tends to intensify as the context length increases, accompanied by shifts in bias patterns.
📍Effect of Context Type on Positional Bias.
LVLMs exhibit more pronounced positional bias in complex context scenarios.
📍Effect of Model Size on Positional Bias.
Positional bias is significantly alleviated as model size increases, consistent with scaling law observed in other capabilities.
📍Effect of Thinking Mode on Positional Bias.
Thinking mode can alleviate the positional bias issue to a certain extent.
Citation
If you find our work helpful for your research, please consider citing our work.
@article{xia2025videolevelgaugeinvestigatingcontextualpositional,
title = {Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models},
author = {Hou, Xia and Fu, Zheren and Ling, Fangcan and Li, Jiajun and Tu, Yi and Mao, Zhendong and Zhang, Yongdong},
journal = {arXiv preprint arXiv:2508.19650},
year = {2025},
}