README.md

metadata

language:
  - en
  - zh
  - fr
license: apache-2.0
size_categories:
  - 1K<n<10K
task_categories:
  - question-answering
  - multiple-choice
pretty_name: >-
  FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question
  Answering
tags:
  - finance
dataset_info:
  features:
    - name: idx
      dtype: int32
    - name: question_id
      dtype: string
    - name: context
      dtype: string
    - name: question
      dtype: string
    - name: options
      sequence: string
    - name: image_1
      dtype: image
    - name: image_2
      dtype: image
    - name: image_3
      dtype: image
    - name: image_4
      dtype: image
    - name: image_5
      dtype: image
    - name: image_6
      dtype: image
    - name: image_7
      dtype: image
    - name: image_type
      dtype: string
    - name: answers
      dtype: string
    - name: explanation
      dtype: string
    - name: topic_difficulty
      dtype: string
    - name: question_type
      dtype: string
    - name: subfield
      dtype: string
    - name: language
      dtype: string
    - name: main_question_id
      dtype: string
    - name: sub_question_id
      dtype: string
    - name: ans_image_1
      dtype: image
    - name: ans_image_2
      dtype: image
    - name: ans_image_3
      dtype: image
    - name: ans_image_4
      dtype: image
    - name: ans_image_5
      dtype: image
    - name: ans_image_6
      dtype: image
    - name: release
      dtype: string
  splits:
    - name: release_v2406
      num_bytes: 88209168.664
      num_examples: 1534
  download_size: 82533346
  dataset_size: 88209168.664
configs:
  - config_name: default
    data_files:
      - split: release_v2406
        path: data/release_v2406-*

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/.

** Note: we are reconstructing the dataset again, which will be finihsed before Feb. **

Dataset Structure

features

• idx：a unique identifier for the index of the question in the dataset.
• question_id: a unique identifier for the question across the whole dataset: {language}{main_question_id}{sub_question_id}_{release_version}.
• 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. (public on release v2406, non-public on the live set release v2501)
• explanation：a detailed justification for the answer. (public on release v2406, non-public on the live set release v2501)
• 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_6： (public on release v2406, non-public on the live set release v2501)

dataset splits

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}
}