{"url":"/dataset/illusionchar-test","name":"IllusionChar_test","full_name":null,"description_markdown":"## IllusionChar_test  \r\n\r\n### Dataset Characteristics  \r\nIllusionChar_test is a generated dataset containing 3,300 samples of images that feature sequences of 3 to 5 random characters. Unlike classification-focused datasets, this dataset is designed for tasks that require reasoning about patterns, sequences, or illusions within the character sequences. All images are synthetically generated, and no real-world data is included.  \r\n\r\n### Motivations and Content Summary  \r\nThe dataset was created using ControlNet for generating images and captions from four large language models (LLMs). It aims to incorporate the phenomenon of *pareidolia*, encouraging models to discern illusions or patterns within character sequences. By focusing on character-based sequences rather than classification, this dataset challenges multimodal models to analyze abstract combinations of symbols and interpret any illusory aspects.  \r\n\r\n### Potential Use Cases  \r\n- **Illusory VQA:** Questioning models about potential illusions or patterns in the character sequences.  \r\n- **Sequence Reasoning Tasks:** Evaluating a model’s ability to interpret and reason about ordered sequences.  \r\n- **Multimodal Model Evaluation:** Benchmarking models on abstract and symbolic data with potential illusions.  \r\n- **Synthetic Data Research:** Exploring synthetic datasets for challenging machine learning models with abstract reasoning tasks.","description_withheld":null,"homepage":"https://huggingface.co/datasets/VQA-Illusion/IllusionChar_test","introduced_date":"2024-12-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/illusory-vqa-benchmarking-and-enhancing","title":"Illusory VQA: Benchmarking and Enhancing Multimodal Models on Visual Illusions","first_author":"Mohammadmostafa Rostamkhani","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Visual Question Answering (VQA)","url":"/task/visual-question-answering","datasets_with_task":"/datasets/task/visual-question-answering"},{"name":"Optical Character Recognition (OCR)","url":"/task/optical-character-recognition","datasets_with_task":"/datasets/task/optical-character-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["IllusionChar_test"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}