README.md

---
license: cc-by-nc-4.0
language:
- ar
- en
pretty_name: ImageEval-ArabicNLP26
tags:
- multimodal
- arabic
- visual-question-answering
- hallucination-detection
- speech
- culture
- text-to-image
- evaluation
configs:
- config_name: task1a_en
  data_files:
  - split: train
    path: task1a/train_en.jsonl
  - split: dev
    path: task1a/dev_en.jsonl
  - split: devtest
    path: task1a/devtest_en.jsonl
  - split: test
    path: task1a/test_en.jsonl
- config_name: task1a_msa
  data_files:
  - split: train
    path: task1a/train_msa.jsonl
  - split: dev
    path: task1a/dev_msa.jsonl
  - split: devtest
    path: task1a/devtest_msa.jsonl
  - split: test
    path: task1a/test_msa.jsonl
- config_name: task1b_en
  data_files:
  - split: train
    path: task1b/train_en.jsonl
  - split: dev
    path: task1b/dev_en.jsonl
  - split: devtest
    path: task1b/devtest_en.jsonl
  - split: test
    path: task1b/test_en.jsonl
- config_name: task1b_msa
  data_files:
  - split: train
    path: task1b/train_msa.jsonl
  - split: dev
    path: task1b/dev_msa.jsonl
  - split: devtest
    path: task1b/devtest_msa.jsonl
  - split: test
    path: task1b/test_msa.jsonl
- config_name: task2
  data_files:
  - split: train
    path: task2/train.jsonl
  - split: dev
    path: task2/dev.jsonl
  - split: test
    path: task2/test.jsonl
---

# ImageEval-ArabicNLP26 👁️

**ImageEval-ArabicNLP26** is the dataset of the [ImageEval 2026 Shared Task](https://imageeval2026.github.io/) at ArabicNLP 2026.
It covers both of the shared task's tasks: **AynVQA** (Task 1), a culturally grounded Arabic multimodal benchmark for spoken visual question answering and hallucination detection, and **CRAI-Bench** (Task 2), which evaluates the cultural accuracy of Arabic text-to-image generation.

The shared task has concluded. All gold labels are released, including the blind test splits, and the official results are on the [leaderboard](https://imageeval2026.github.io/leaderboard/).

## 💬 Join our Slack

Join the **[ImageEval Slack channel](https://join.slack.com/t/mm-eval/shared_invite/zt-44zii4e53-IiZZ3O73Za9XL4umb9mjUQ)** for announcements about data
releases, deadlines, and updates, and to connect with the organisers and other
participants.

## 🎯 Tasks

**Spoken VQA (Task 1a).** Given an image and the spoken question and options
(audio), choose the correct option.

Prediction: the option index 0, 1 or 2.

**Hallucination detection (Task 1b).** Given an image and three statements, decide for **each** statement whether it is **True** (grounded in the image) or **False** (a hallucination). Exactly one statement is grounded.

Prediction: a True/False label per statement.

**Cultural accuracy evaluation (Task 2).** Given a reference image of a Qatari cultural scene, a caption, and an AI-generated image produced from that caption, judge how faithfully the generated image represents the culture.

Prediction: CRAI scores across five dimensions plus a composite.

# 👁️ Task 1: AynVQA

Ayn (عين, "eye") tests whether a model can read a culturally specific image, both from a spoken Arabic question and by telling grounded descriptions apart from plausible but hallucinated ones. Each subtask is offered as two language tracks, **English** and **Modern Standard Arabic (MSA)**, scored separately.

Starter kit, format checker and official scorer: [ImageEval2026-tasks/task1](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task1).

## 🗂️ Subsets

| config | task | language | Codabench |
|---|---|---|---|
| `task1a_en`  | Spoken VQA | English | [compete](https://www.codabench.org/competitions/17049/) |
| `task1a_msa` | Spoken VQA | MSA | [compete](https://www.codabench.org/competitions/17048/) |
| `task1b_en`  | Hallucination | English | [compete](https://www.codabench.org/competitions/17051/) |
| `task1b_msa` | Hallucination | MSA | [compete](https://www.codabench.org/competitions/17050/) |

The English and MSA tracks of a task are parallel: same images, same answers, and
the questions are translations of each other.

## 🌍 Countries

Task 1 spans **18 Arab countries** (Task 2 is grounded specifically in Qatari culture):

Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, UAE, Yemen.


## 🔊 Audio

The Task 1a questions in `train`, `dev` and `devtest` are **synthetically generated**
using voice cloning (TTS). The questions in the final blind **test** set will be
**human-recorded**; expect a speaker/recording-condition shift between the
dev-phase audio and the test audio.

## 📂 Files

```
images/<id>.jpg            one image per item, shared across tasks and languages
audio/<lang>/<id>.wav      spoken question and options (Task 1a)
task1a/<split>_<lang>.jsonl
task1b/<split>_<lang>.jsonl
```

Media is referenced by relative path keyed on `id`, so inputs join to files
directly.

### 🏷️ Fields

Task 1a (`task1a/<split>_<lang>.jsonl`):

| field | type | description |
|---|---|---|
| `id` | str | item id |
| `image` | str | `images/<id>.jpg` |
| `audio` | str | `audio/<lang>/<id>.wav`, the spoken question **and** the three options (no text is given; listen and answer) |
| `label` | int | index (0–2) of the correct option |

Task 1b (`task1b/<split>_<lang>.jsonl`):

| field | type | description |
|---|---|---|
| `id` | str | item id |
| `image` | str | `images/<id>.jpg` |
| `statements` | list[str] | three statements, exactly one grounded |
| `labels` | list[bool] | truth value of each statement (one `true`) |

Every split, including `devtest` and `test`, now carries the full schema:
labels plus `country`, `category` and `subcategory`. The competition has ended
and the previously blind splits are fully labelled.

## 📊 Splits

| split | labels | items | use |
|---|---|---|---|
| `train`   | yes | 3000 | training and fine-tuning |
| `dev`     | yes | 500  | validation |
| `devtest` | yes | 500  | development-phase evaluation (labels now released) |
| `test`    | yes | 1000 | official competition ranking (labels now released) |




## 📏 Evaluation

Each of the four tracks is scored separately. One **ranking metric** decides the
leaderboard; the remaining columns are reported as diagnostics. A missing or
unparseable prediction always counts as wrong.

### Spoken VQA
A 3-way multiple choice: predict the option index `0`, `1` or `2`.

| metric | role | meaning |
|---|---|---|
| **Accuracy** | **ranking** | fraction of items answered correctly |
| Balanced accuracy | reported | mean per-class recall over the three positions |
| Macro-F1 | reported | macro-averaged F1 over the three positions |

### Hallucination detection
Three statements per image; predict **True/False** for each. Exactly one statement is
grounded ("Q+"); the other two are hallucinated ("Q−").

| metric | role | meaning |
|---|---|---|
| **Contrastive Instability (CI)** | **ranking** | of the items with at least one of the three statements correct, the fraction that are not fully correct (all three right); **lower is better** |
| Combined accuracy | reported | fraction of items where **all three** labels are correct (grounded → true, both hallucinated → false); higher is better |
| CFHR | reported | of items where the grounded statement was correctly identified, the fraction that still affirmed a hallucinated one; **lower is better** |
| Q+ accuracy | reported | grounded statement correctly marked true |
| Q− accuracy | reported | hallucinated statements correctly marked false (over all false statements) |

True/False is read from the prediction with the shared‑task `evaluate_tf` parser,
which handles English and Arabic verdicts (e.g. `true`/`false`, صح/خطأ).

## 🧪 Baselines & example notebooks

Starter **Colab notebooks** that run end to end (download the data → run a model →
write a Codabench-ready `prediction.zip`) are here:

**[Reference notebooks (Google Drive)](https://drive.google.com/drive/folders/1zDO2uXq0fEQhfNj8M2DstkNL1-EEXybT?usp=sharing)**

- **Open-model baseline**: Task 1a (`Qwen2.5-Omni`) and Task 1b (`Qwen2.5-VL`),
  runnable on a free Colab T4 (4-bit).
- **Cascaded API example (Task 1a)**: Fanar `Aura-STT` (speech → text) →
  `Oryx` (image understanding); no GPU required.

These are references only; participants are free to use their own models,
prompts, and configurations.

Reference baseline scores (greedy decoding, `do_sample=False`, so deterministic).
Contrastive instability is lower-is-better:

| task | track | model | ranking metric | score |
|---|---|---|---|---|
| 1a | English | Qwen2.5-Omni | accuracy | 0.6640 |
| 1a | MSA     | Qwen2.5-Omni | accuracy | 0.3980 |
| 1b | English | Qwen2.5-VL   | contrastive instability | 0.3133 |
| 1b | MSA     | Qwen2.5-VL   | contrastive instability | 0.4900 |

For 1b, combined accuracy was 0.6840 (English) and 0.5080 (MSA).

# 🎨 Task 2: CRAI-Bench

**CRAI-Bench** evaluates whether AI-generated images faithfully represent Qatari and Arab cultural scenes. Given a reference image of an authentic cultural scene, a caption, and an AI-generated image produced from that caption, systems predict scores on the **Cultural Representation Accuracy Index (CRAI)**, a five-dimensional framework validated against human annotation. Each of the reference images has five caption versions, ranging from fully Qatari-specific (v1) to entirely generic (v5). See the [Task 2 page](https://imageeval2026.github.io/task2/) for the full framework and scoring rubric.

Starter kit, format checker, baselines and the official scorer: [ImageEval2026-tasks/task2](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task2).

## 📂 Files

One jsonl per split; each row is one instance with its caption, image paths
and the human gold scores:

```
task2/<split>.jsonl                  id, image_id, version, caption, ref_image, gen_image, category, CRAI_* scores
task2/<split>/imgs/ref/              reference images (one per image_id)
task2/<split>/imgs/generated/        generated images (one per id)
```

## 📊 Splits

| split | reference images | instances (5 caption versions each) | gold |
|---|---|---|---|
| `train` | 24 | 120 | yes |
| `dev`   | 8  | 40  | yes |
| `test`  | 8  | 40  | yes (released after the competition) |

## 📏 Evaluation

| metric | role | meaning |
|---|---|---|
| **Spearman** | **ranking** | correlation between predicted and gold `CRAI_composite` |
| MAE | tiebreaker | mean absolute error on `CRAI_composite` (lower is better) |

## 🧪 Baselines

From the [starter kit](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task2/baselines), on dev:

| system | Spearman | MAE |
|---|---|---|
| Mean baseline | N/A | 0.3450 |
| GPT-4 LLM-as-a-judge | 0.6250 | 0.2248 |

## 📜 License and contact
The dataset is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). 

- Website: https://imageeval2026.github.io/
- Slack: https://join.slack.com/t/mm-eval/shared_invite/zt-44zii4e53-IiZZ3O73Za9XL4umb9mjUQ
- Contact: imageeval2026@gmail.com


## Citation

The shared task overview paper covers both tasks. If you use the Task 1 data,
please also cite the dataset papers below.

```
@inproceedings{imageeval-2026,
    title = {{ImageEval 2026}: Culturally Grounded {A}rabic Multimodal Evaluation},
    author = {Abdaljalil, Samir and
              Bhatti, Hunzalah Hassan and
              Bashiti, Ahlam and
              Amir, Farina and
              Hasan, Md Arid and
              Mousi, Basel and
              Durrani, Nadir and
              Dalvi, Fahim and              
              Sheikh Ali, Zien and
              Serpedin, Erchin and
              Kurban, Hasan and
              Jarrar, Mustafa and              
              Chowdhury, Shammur Absar and
              Alam, Firoj},
    booktitle = {Proceedings of the Fourth Arabic Natural Language Processing Conference: Shared Tasks},
    month = oct,
    year = {2026},
    address = {Budapest, Hungary},
    publisher = {Association for Computational Linguistics}
}

@article{alam2025everydaymmqa,
  title = {{OASIS}: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA},
  author = {Alam, Firoj and Shahroor, Ali Ezzat and Hasan, Md. Arid and Ali, Zien Sheikh and Bhatti, Hunzalah Hassan and Kmainasi, Mohamed Bayan and Chowdhury, Shammur Absar and Mousi, Basel and Dalvi, Fahim and Durrani, Nadir and Milic-Frayling, Natasa},
  journal = {arXiv preprint arXiv:2510.06371},
  year = {2025},
}

@inproceedings{mousi-etal-2026-correct,
    title = "Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models",
    author = "Mousi, Basel  and
      Dalvi, Fahim  and
      Chowdhury, Shammur Absar  and
      Alam, Firoj  and
      Durrani, Nadir",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.234/",
    doi = "10.18653/v1/2026.findings-acl.234",
    pages = "4763--4788",
    ISBN = "979-8-89176-395-1",
}


@inproceedings{mousi2026said,
  title     = {Said Aloud, Read Different: Cross-Modal Instability in Multimodal Models},
  author    = {Mousi, Basel and Dalvi, Fahim and Chowdhury, Shammur and Alam, Firoj and Durrani, Nadir},
  booktitle = {Proceedings of Interspeech 2026},
  year      = {2026},
  address   = {Sydney, Australia},
  note = {accepted}
}

```