weaverbirdllm/famma

Introduction

FAMMA dataset consists of 1,758 meticulously collected multimodal questions. The questions encompass three heterogeneous image types - tables, charts and text & math screenshots - and span eight subfields in finance, comprehensively covering topics across major asset classes. Additionally, all the questions are categorized by three difficulty levels — easy, medium, and hard - and are available in three languages — English, Chinese, and French. Furthermore, the questions are divided into two types: multiple-choice and open questions.

The leaderboard is regularly updated and can be accessed at https://famma-bench.github.io/famma/.

Dataset Structure

features

• question_id: a unique identifier for the question across the whole dataset.
• context: relevant background information related to the question.
• question: the specific query being asked.
• options: the specific query being asked.
• image_1- image_7: directories of images referenced in the context or question.
• image_type: type of the image, e.g., chart, table, screenshot.
• answers: a concise and accurate response. (non-public on the test set for the moment)
• explanation：a detailed justification for the answer. (non-public on the test set for the moment)
• topic_difficulty: a measure of the question's complexity based on the level of reasoning required.
• question_type: categorized as either multiple-choice or open-ended.
• subfield: the specific area of expertise to which the question belongs, categorized into eight subfields.
• language：the language in which the question text is written.
• main_question_id：a unique identifier for the question within its context; questions with the same context share the same ID.
• sub_question_id：a unique identifier for the question within its corresponding main question.
• ans_image_1 - ans_image_4： (non-public on the test set for the moment)

dataset splits

Chinese subset

• splits:
  • name: validation
    • num_bytes: 530067.0
    • num_examples: 19
  • name: test
    • num_bytes: 10497574.0
    • num_examples: 234

English subset

• splits:
  • name: validation
    • num_bytes: 19326545.0
    • num_examples: 88
  • name: test
    • num_bytes: 235713843.904
    • num_examples: 1297

French subset

• splits:
  • name: validation
    • num_bytes: 1945622.0
    • num_examples: 13
  • name: test
    • num_bytes: 14026200.0
    • num_examples: 107

Citation

If you use FAMMA in your research, please cite our paper as follows:

@article{xue2024famma,
  title={FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering},
  author={Siqiao Xue, Tingting Chen, Fan Zhou, Qingyang Dai, Zhixuan Chu, and Hongyuan Mei},
  journal={arXiv preprint arXiv:2410.04526},
  year={2024},
  url={https://arxiv.org/abs/2410.04526}
}