# ImageEval 2026: Culturally Grounded Arabic Multimodal Evaluation

arXiv: https://arxiv.org/abs/2608.30475 · HTML: https://mlsanity.com/paper/2608.30475/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 53 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 27 of 27)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation](https://mlsanity.com/paper/2311.07397/) (2023)
- [CVQA: Culturally-diverse Multilingual Visual Question Answering Benchmark](https://mlsanity.com/paper/2406.05967/) (2024)
- [MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmark](https://mlsanity.com/paper/2506.04779/) (2025)
- [Benchmarking Vision Language Models for Cultural Understanding](https://mlsanity.com/paper/2407.10920/) (2024)
- [Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models](https://mlsanity.com/paper/2310.05338/) (2023)
- [Spoken SQuAD: A Study of Mitigating the Impact of Speech Recognition Errors on Listening Comprehension](https://mlsanity.com/paper/1804.00320/) (2018)
- [xGQA: Cross-Lingual Visual Question Answering](https://mlsanity.com/paper/2109.06082/) (2021)
- [Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites](https://mlsanity.com/paper/2312.01701/) (2023)
- [End-to-end Spoken Conversational Question Answering: Task, Dataset and Model](https://mlsanity.com/paper/2204.14272/) (2022)
- [A Survey of Multimodal Hallucination Evaluation and Detection](https://mlsanity.com/paper/2507.19024/) (2025)
- [Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models](https://mlsanity.com/paper/2312.02219/) (2023)
- [CAMEL-Bench: A Comprehensive Arabic LMM Benchmark](https://mlsanity.com/paper/2410.18976/) (2024)
- [Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic](https://mlsanity.com/paper/2407.18129/) (2024)
- [CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics](https://mlsanity.com/paper/2506.08835/) (2025)
- [Diffusion Models Through a Global Lens: Are They Culturally Inclusive?](https://mlsanity.com/paper/2502.08914/) (2025)
- [The Landscape of Arabic Large Language Models (ALLMs): A New Era for Arabic Language Technology](https://mlsanity.com/paper/2506.01340/) (2025)
- [AraHalluEval: A Fine-grained Hallucination Evaluation Framework for Arabic LLMs](https://mlsanity.com/paper/2509.04656/) (2025)
- [Culture in Action: Evaluating Text-to-Image Models through Social Activities](https://mlsanity.com/paper/2511.05681/) (2025)
- [SpokenNativQA: Multilingual Everyday Spoken Queries for LLMs](https://mlsanity.com/paper/2505.19163/) (2025)
- [When Cultures Meet: Multicultural Text-to-Image Generation](https://mlsanity.com/paper/2502.15972/) (2025)
- [Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset](https://mlsanity.com/paper/2505.21979/) (2025)
- [OASIS: A Multilingual and Multimodal Dataset for Culturally Grounded Spoken Visual QA](https://mlsanity.com/paper/2510.06371/) (2025)
- [Evaluating AI-Generated Images of Cultural Artifacts with Community-Informed Rubrics](https://mlsanity.com/paper/2604.02406/) (2026)
- [Said Aloud, Read Different: Cross-Modal Instability in Multimodal Models](https://mlsanity.com/paper/2608.27135/) (2026)
- [WASIL: In-the-Wild Arabic Spoken Interactions with LLMs](https://mlsanity.com/paper/2605.16364/) (2026)

## Abstract (arXiv)

We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question answering and image-grounded hallucination detection in English and Modern Standard Arabic (MSA), and (ii) CRAI-Bench, evaluating the cultural accuracy of text-to-image generation. A total of 14 teams participated in the test phase, with 12 teams submitting system description papers. Participating systems used a range of approaches, including zero-shot prompting, fine-tuning of vision-language models, speech-recognition pipelines, ensembling, and score calibration. We describe the task setup, datasets, evaluation procedure, and participating systems, and summarize the main results across the different tracks. All datasets and evaluation scripts from the shared task are released to the research community. The shared task highlights the challenges of culturally grounded multimodal evaluation, particularly for Arabic speech and image-text reasoning.