IllusionChar_test

홈페이지 · 논문 1편

## IllusionChar_test ### Dataset Characteristics IllusionChar_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. ### Motivations and Content Summary The 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. ### Potential Use Cases - Illusory VQA: Questioning models about potential illusions or patterns in the character sequences. - Sequence Reasoning Tasks: Evaluating a model’s ability to interpret and reason about ordered sequences. - Multimodal Model Evaluation: Benchmarking models on abstract and symbolic data with potential illusions. - Synthetic Data Research: Exploring synthetic datasets for challenging machine learning models with abstract reasoning tasks.