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ImageEval2026-Task1-AynVQA

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speech
culture
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ImageEval 2026 Task 1 (Ayn-VQA): train/dev/devtest + Codabench links

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+ ---
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+ license: cc-by-nc-4.0
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+ language:
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+ - ar
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+ - en
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+ pretty_name: 'ImageEval 2026, Task 1: Ayn-VQA'
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+ tags:
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+ - multimodal
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+ - arabic
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+ - visual-question-answering
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+ - hallucination-detection
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+ - speech
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+ - culture
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+ configs:
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+ - config_name: task1a_en
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+ data_files:
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+ - split: train
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+ path: task1a/train_en.jsonl
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+ - split: dev
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+ path: task1a/dev_en.jsonl
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+ - split: devtest
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+ path: task1a/devtest_en.jsonl
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+ - config_name: task1a_msa
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+ data_files:
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+ - split: train
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+ path: task1a/train_msa.jsonl
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+ - split: dev
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+ path: task1a/dev_msa.jsonl
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+ - split: devtest
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+ path: task1a/devtest_msa.jsonl
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+ - config_name: task1c_en
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+ data_files:
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+ - split: train
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+ path: task1c/train_en.jsonl
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+ - split: dev
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+ path: task1c/dev_en.jsonl
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+ - split: devtest
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+ path: task1c/devtest_en.jsonl
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+ - config_name: task1c_msa
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+ data_files:
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+ - split: train
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+ path: task1c/train_msa.jsonl
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+ - split: dev
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+ path: task1c/dev_msa.jsonl
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+ - split: devtest
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+ path: task1c/devtest_msa.jsonl
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+ ---
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+
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+ # ImageEval 2026, Task 1: Ayn-VQA 👁️
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+
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+ Culturally grounded Arabic multimodal evaluation, part of the
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+ [ImageEval 2026 Shared Task](https://imageeval2026.github.io/) at ArabicNLP 2026.
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+ Ayn (عين, "eye") tests whether a model can read a culturally specific image, both
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+ from a spoken Arabic question and by telling grounded descriptions apart from
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+ plausible but hallucinated ones.
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+
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+ Each task is offered as two language tracks, **English** and **Modern Standard
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+ Arabic (MSA)**, scored separately.
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+
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+ ## 📝 Register
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+
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+ Please fill in the **[registration form](https://docs.google.com/forms/d/e/1FAIpQLSd1QKF4rXD_gbLJlDykLvB0DGMIogwhraeOtWRiQiotucK0zA/viewform)**
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+ — it lets the organisers keep track of participants and notify you about data
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+ releases, deadlines, and any updates.
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+
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+ ## 🎯 Tasks
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+
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+ **Task 1a, Spoken VQA.** Given an image and the spoken question and options
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+ (audio), choose the correct option.
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+
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+ Prediction: the option index 0, 1 or 2.
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+
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+ **Task 1c, Hallucination detection.** Given an image and three statements, decide
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+ for **each** statement whether it is **True** (grounded in the image) or **False**
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+ (a hallucination). Exactly one statement is grounded.
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+
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+ Prediction: a True/False label per statement.
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+
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+ ## 🗂️ Subsets
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+
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+ | config | task | language | Codabench |
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+ |---|---|---|---|
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+ | `task1a_en` | Spoken VQA | English | [compete](https://www.codabench.org/competitions/16955/) |
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+ | `task1a_msa` | Spoken VQA | MSA | [compete](https://www.codabench.org/competitions/16956/) |
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+ | `task1c_en` | Hallucination | English | [compete](https://www.codabench.org/competitions/16957/) |
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+ | `task1c_msa` | Hallucination | MSA | [compete](https://www.codabench.org/competitions/16958/) |
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+
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+ The English and MSA tracks of a task are parallel: same images, same answers, and
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+ the questions are translations of each other.
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+
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+ ## 🌍 Countries
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+
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+ The dataset spans **18 Arab countries**:
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+
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+ Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, UAE, Yemen.
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+
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+
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+ ## 🔊 Audio
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+
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+ The Task 1a questions in `train`, `dev` and `devtest` are **synthetically generated**
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+ using voice cloning (TTS). The questions in the final blind **test** set will be
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+ **human-recorded**; expect a speaker/recording-condition shift between the
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+ dev-phase audio and the test audio.
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+
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+ ## 📂 Files
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+
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+ ```
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+ images/<id>.jpg one image per item, shared across tasks and languages
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+ audio/<lang>/<id>.wav spoken question and options (Task 1a)
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+ task1a/<split>_<lang>.jsonl
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+ task1c/<split>_<lang>.jsonl
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+ ```
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+
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+ Media is referenced by relative path keyed on `id`, so inputs join to files
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+ directly.
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+
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+ ### Fields
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+
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+ Task 1a (`task1a/<split>_<lang>.jsonl`):
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+
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+ | field | type | description |
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+ |---|---|---|
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+ | `id` | str | item id |
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+ | `image` | str | `images/<id>.jpg` |
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+ | `audio` | str | `audio/<lang>/<id>.wav`, the spoken question **and** the three options (no text is given; listen and answer) |
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+ | `label` | int | index (0–2) of the correct option |
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+
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+ Task 1c (`task1c/<split>_<lang>.jsonl`):
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+
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+ | field | type | description |
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+ |---|---|---|
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+ | `id` | str | item id |
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+ | `image` | str | `images/<id>.jpg` |
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+ | `statements` | list[str] | three statements, exactly one grounded |
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+ | `labels` | list[bool] | truth value of each statement (one `true`) |
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+
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+ `train` and `dev` additionally include `country`, `category` and `subcategory`.
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+ These and the labels are **not** provided in `devtest`, nor will they be provided in `test`.
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+
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+ ## 📊 Splits
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+
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+ | split | labels | items | use |
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+ |---|---|---|---|
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+ | `train` | yes | 3000 | training and fine-tuning |
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+ | `dev` | yes | 500 | validation |
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+ | `devtest` | no | 500 | pre-competition; submit to Codabench |
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+ | `test` | no | 1000 | competition |
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+
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+ The blind **test** set is released later for the final phase.
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+
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+ ## 🗓️ Timeline
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+
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+ | phase | window | submit on |
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+ |---|---|---|
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+ | **Development** | `2026-05-22 → 2026-07-19` | `devtest` — leaderboard live |
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+ | **Testing** | `2026-07-20 → 2026-07-29` | `test` �� blind, final ranking |
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+
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+ Dates may shift — watch the [website](https://imageeval2026.github.io/) and the
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+ [registration form](https://docs.google.com/forms/d/e/1FAIpQLSd1QKF4rXD_gbLJlDykLvB0DGMIogwhraeOtWRiQiotucK0zA/viewform)
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+ for announcements.
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+
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+ ## 🚀 Submitting
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+
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+ 1. Load the subset you are targeting:
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+ ```python
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+ from datasets import load_dataset
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+ ds = load_dataset("QCRI/ImageEval2026-Task1-AynVQA", "task1c_msa", split="devtest")
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+ ```
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+ 2. Produce predictions:
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+ - **Task 1a** — for each item, predict an index 0, 1 or 2. Write a CSV with
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+ columns `id,prediction`:
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+ ```
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+ id,prediction
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+ 1dcdf6da...,0
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+ 803ca9b8...,2
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+ ```
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+ - **Task 1c** — for each item, predict True/False for each of the three
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+ statements. Write a CSV with columns `id,statement_index,prediction`
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+ (`statement_index` is 0–2; `prediction` is `true` or `false`):
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+ ```
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+ id,statement_index,prediction
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+ 1dcdf6da...,0,false
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+ 1dcdf6da...,1,true
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+ 1dcdf6da...,2,false
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+ ```
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+ 3. Zip the CSV as `prediction.zip` and submit to the matching Codabench
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+ competition (links in the **Subsets** table above):
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+ [task1a_en](https://www.codabench.org/competitions/16955/) ·
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+ [task1a_msa](https://www.codabench.org/competitions/16956/) ·
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+ [task1c_en](https://www.codabench.org/competitions/16957/) ·
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+ [task1c_msa](https://www.codabench.org/competitions/16958/).
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+
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+ **Metrics.** Task 1a: **accuracy**. Task 1c: **combined accuracy** (all three
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+ statements correct, primary), with the hallucination rate and the True / False
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+ (Q+ / Q−) accuracies reported alongside.
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+
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+ ## 📜 License and contact
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+
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+ CC BY-NC 4.0, research use only.
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+
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+ - Website: https://imageeval2026.github.io/
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+ - Tasks repo: https://github.com/ImageEval2026/ImageEval2026-tasks
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+ - Contact: imageeval2026@gmail.com
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