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--- |
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dataset_info: |
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features: |
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- name: landmark_id |
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dtype: int64 |
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- name: country_code |
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dtype: string |
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- name: domestic_language_code |
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dtype: string |
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- name: language_code |
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dtype: string |
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- name: landmark_name |
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dtype: string |
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- name: prompt_idx |
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dtype: int64 |
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splits: |
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- name: test |
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num_bytes: 470104 |
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num_examples: 8100 |
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- name: debug |
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num_bytes: 548 |
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num_examples: 10 |
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download_size: 80893 |
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dataset_size: 470652 |
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configs: |
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- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
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- split: debug |
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path: data/debug-* |
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license: cc |
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task_categories: |
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- text-generation |
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language: |
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- ar |
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- zh |
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- en |
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- fr |
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- de |
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- it |
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- ja |
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- pt |
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- es |
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size_categories: |
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- 1K<n<10K |
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tags: |
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- Image |
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- Text |
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- Multilingual |
|
--- |
|
|
|
|
|
<a href="https://arxiv.org/abs/2505.15075" target="_blank"> |
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<img alt="arXiv" src="https://img.shields.io/badge/arXiv-traveling--across--languages-red?logo=arxiv" height="20" /> |
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</a> |
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<a href="https://github.com/nlp-waseda/traveling-across-languages" target="_blank" style="display: inline-block; margin-right: 10px;"> |
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<img alt="GitHub Code" src="https://img.shields.io/badge/Code-traveling--across--languages-white?&logo=github&logoColor=white" /> |
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</a> |
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|
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# VisRecall |
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This repository contains the VisRecall benchmark, introduced in [Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs](https://arxiv.org/abs/2505.15075). |
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|
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## Dataset Description |
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Imagine a tourist finished their journey in Japan and came back to France, eager to share the places they visited with their friends. |
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When portraying these experiences, the visual information they convey is inherently independent of language, meaning that descriptions created in different languages should ideally be highly similar. |
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This concept extends to MLLMs as well. |
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While a model may demonstrate decent consistency in VQA tasks, any inconsistency in generation tasks would lead to a biased user experience (i.e., a knowing vs saying distinction). |
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To assess the cross-lingual consistency of "visual memory" in MLLMs, we introduce VisRecall, a multilingual benchmark designed to evaluate visual description generation across 450 landmarks in 9 languages. |
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|
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The dataset contains the following fields: |
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|
|
| Field Name | Description | |
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| :----------------------- | :-------------------------------------------------------------------------- | |
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| `landmark_id` | Unique identifier for the landmark in the dataset. | |
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| `domestic_language_code` | ISO 639 language code of the official language spoken in the country where the landmark is located. | |
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| `language_code` | ISO 639 language code of the prompt. | |
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| `country_code` | ISO country code representing the location of the landmark. | |
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| `landmark_name` | Name of the landmark used for evaluation. | |
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| `prompt_idx` | Index of the prompt used. Each language includes two distinct prompts. | |
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|
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Additionally, the following files are necessary for running evalutaion: |
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| File Name | Description | |
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| :-------------------- | :---------------------------------------------------------------------- | |
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| `images.tar.gz` | Compressed archive containing images of landmarks, used for CLIPScore calculation. | |
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| `images_list.json` | List of image file paths included in the dataset. | |
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| `landmark_list.json` | Metadata for each landmark, including IDs, names, etc. | |
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|
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## Evaluation |
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Please refer to our [GitHub repository](https://github.com/nlp-waseda/traveling-across-languages) for detailed information on the evaluation setup. |
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|
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## Citation |
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|
|
```bibtex |
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@misc{wang2025travelinglanguagesbenchmarkingcrosslingual, |
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title={Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs}, |
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author={Hao Wang and Pinzhi Huang and Jihan Yang and Saining Xie and Daisuke Kawahara}, |
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year={2025}, |
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eprint={2505.15075}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.15075}, |
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} |
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``` |