---
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: is_arithmetic
    dtype: int32
  - 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_livepro
    num_bytes: 3266580.0
    num_examples: 103
  - name: release_basic
    num_bytes: 113235537.37
    num_examples: 1945
  - name: release_basic_txt
    num_bytes: 1978313.375
    num_examples: 1945
  download_size: 94674468
  dataset_size: 118480430.745
configs:
- config_name: default
  data_files:
  - split: release_livepro
    path: data/release_livepro-*
  - split: release_basic
    path: data/release_basic-*
  - split: release_basic_txt
    path: data/release_basic_txt-*
---
## Introduction 



`FAMMA` is a multi-modal financial Q&A benchmark dataset. 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.

More importantly, `FAMMA` provides a "live" benchmark for evaluating financial analysis capabilities of LLMs. The benchmark continuously collects new questions from real-world financial professionals, ensuring up-to-date and contamination-free evaluation. 

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

The project code is available at https://github.com/famma-bench/bench-script.



## NEWS

🔥 **Latest Updates**:
- [2025/03]  Release of `release_basic_txt`, a purely textual dataset that utilizes OCR to extract multimodal information and convert it into textual context for each question in `release_basic`.
- [2025/03]  Add `is_arithmetic` column in the dataset to indicate whether the question involves heavy compuation. 
- [2025/02]  Release of `release_livepro` dataset.
- [2025/01]  Release of `release_basic` dataset, now including answers and explanations with enhanced quality.
- [2024/06]  Initial public release of `FAMMA` benchmark (based on the `release_basic` dataset), along with our paper: [FAMMA: A Benchmark for Financial Domain Multilingual Multimodal Question Answering](https://arxiv.org/abs/2410.04526).


## Live Benchmarking Concept

In addition to the baseline dataset (`release_basic` that contains 1935 questions), `FAMMA` provides a `live` benchmark for evaluating financial analysis capabilities of LLMs. The benchmark continuously collects new questions from real-world financial professionals, ensuring up-to-date and contamination-free evaluation. 

The "live" nature of FAMMA means:
1. **Expert-Sourced Questions**: New questions are continuously proposed by financial experts, ensuring they have never been made public before and reflect real-world financial analysis scenarios. See [contributors](https://github.com/famma-bench/bench-script/blob/main/contributors.md).
2. **Contamination Prevention**: Questions in the live set (at the moment `release_livepro`) have non-public answers and explanations.
3. **Time-Based Evaluation**: Models can be evaluated on questions from specific time periods.
4. **Domain Coverage**: Questions span across different financial topics and complexity levels, curated by domain experts.

## Dataset Versions

FAMMA is continuously updated with new questions. We provide different versions of the dataset:

* `release_basic`: The release containing 1935 questions, collected from online sources. Apart from the questions, both answers and explanations are provided.
* `release_livepro`: The release containing 103 questions, created by invited experts. Only the questions are provided.
  

##  Dataset Structure 

- 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_basic`, non-public on the live set `release_livepro`)**
- explanation: a detailed justification for the answer. **(public on `release_basic`, non-public on the live set `release_livepro`)**
- 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 under the same language subset 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.
- is_arithmetic: whether the question is an arithmetic question that needs heavy calculation.
- ans_image_1 - ans_image_6: **(public on `release_basic`, non-public on the live set `release_livepro`)**
 

## Download 

see the script at https://github.com/famma-bench/bench-script/blob/main/step_1_download_dataset.py


Fristly, clone the repository and install the dependencies:
```bash
git clone https://github.com/famma-bench/bench-script.git
cd bench-script
pip install -r requirements.txt
```

To download the dataset, run the following command:


```bash
python step_1_download_dataset.py \
    --hf_dir "weaverbirdllm/famma" \
    --split "release_basic" \ # or "release_livepro" or None to download the whole set
    --save_dir "./hf_data"
```

Options:
- `--hf_dir`: HuggingFace repository name
- `--split`: Specific version to download (optional)
- `--save_dir`: Local directory to save the dataset (default: "./hf_data")


After downloading, the dataset will be saved in the local directory `./data` in json format. 

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

```latex
@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}
}

```