MathScape

This is the codebase for the paper MathScape: Evaluating MLLMs in multimodal Math Scenarios through a Hierarchical Benchmark.

Environments

To set up the environment, use the following commands:

conda create -n MathScape python=3.10
conda activate MathScape

Then, install the required Python packages:

pip install -r requirements.txt

Data Description

The original dataset comprised 1,369 entries. After manually removing erroneous data, 1,325 entries remain. The images in these 1,325 entries have been renumbered sequentially from 1 to 1,325.

• math_data.json
  All the math data is in JSON form.

• math_question_solution_ans.json
  This file contains math questions along with their corresponding image IDs, detailed solution processes, and reference standard answers.

• math_with_class.jsonl
  This file decomposes each question into multiple sub-questions (e.g., a single question may consist of 2-3 sub-questions). It includes type labels for each sub-question, knowledge point labels, solution processes for each sub-question, and reference standard answers for each sub-question.

• question_knowledge.json
  This file maps the image IDs of the questions to their corresponding knowledge point classifications.

  You can download the dataset from this link

MathScape Evaluation

1. Model Evaluation

To evaluate multiple models, you can configure the models in the model.py file. This script is responsible for loading the models, processing the questions, and generating the results.

model.py

2. Running Evaluations

To evaluate the models' performance, run:

eval.py

This script compares the model-generated solutions against the standard answers, evaluates the results, and saves the evaluation data in a jsonl file.

3. Assessing Model Performance

Once all models have been evaluated and the jsonl files generated, you can assess the performance of each model.

3.1 Judge all the Performances

To assess overall performance, run:

judge_all.py

This script determines the correctness of each answer, calculates the overall average accuracy for each model, and includes functions like judge_by_xx to read and evaluate the results from the jsonl file. It also allows for evaluating accuracy based on various dimensions such as knowledge points, educational stages, and question types.

3.2 Judge by Knowledge Points

To evaluate performance based on specific knowledge points, run:

python judge_by_knowledge.py

3.3 Judge by Educational Stages

To evaluate performance across different educational stages, run:

python judge_by_stage.py

3.4 Judge by Question Types

To evaluate performance based on question types, run:

python judge_by_type.py