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# MMSci_Table
Dataset for the paper "[Does Table Source Matter? Benchmarking and Improving Multimodal Scientific Table Understanding and Reasoning](s)"
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📑 <a href="https://arxiv.org/pdf/">Paper</a>    </a>
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# MMSci Dataset Collection
The MMSci dataset collection consists of three complementary datasets designed for scientific multimodal table understanding and reasoning: MMSci-Pre, MMSci-Ins, and MMSci-Eval.
## Dataset Summary
- **MMSci-Pre**: A domain-specific pre-training dataset containing 52K scientific table structure recognition samples
- **MMSci-Ins**: An instruction tuning dataset with 12K samples across three table-based tasks
- **MMSci-Eval**: A benchmark with 3,114 testing samples for numerical reasoning evaluation
## Framework Overview
*Figure 1: Overview of the MMSci framework showing the four key stages: Table Image Generation, Dataset Construction, Table Structure Learning, and Visual Instruction Tuning.*
## Dataset Details
### MMSci-Pre
- **Size**: 52K samples
- **Format**: Table image-to-HTML pairs
- **Source**: Scientific papers from SciGen dataset
- **Purpose**: Table structure learning and alignment of visual features with textual representations
- **Features**:
- High-quality HTML format tables
- Rendered table images preserving structural integrity
- Complex layouts and relationships from scientific papers
- Focus on tables with significant numerical values
*Figure 2: Example from MMSci-Pre dataset showing the table image and its corresponding HTML representation.*
### MMSci-Ins
- **Size**: 12K samples
- **Format**: Instruction-following samples with reasoning steps
- **Tasks**:
- Table Question Answering (TQA)
- Table Fact Verification (TFV)
- Table-to-Text Generation (T2T)
- **Features**:
- Detailed step-by-step reasoning processes
- Balanced distribution across three tasks
- Each table paired with one TQA, TFV, and T2T task
- Built upon scientific domain tables
*Figure 3: Example from MMSci-Ins dataset showing instruction-following samples across different tasks.*
### MMSci-Eval
- **Size**: 3,114 samples
- **Purpose**: Comprehensive evaluation of numerical reasoning capabilities
- **Features**:
- Testing samples across TQA, TFV, and T2T tasks
- Focus on numerical reasoning assessment
- Based on SciGen dataset test set
- Diverse reasoning types and complexity levels
## Dataset Creation
The datasets were created through a rigorous process:
1. Collection of raw tabular data from SciGen dataset
2. Transformation of textual tables into HTML format
3. Rendering of HTML tables into high-quality images
4. Generation of instruction-following samples with reasoning steps
5. Quality assurance through balanced task distribution
## Intended Uses
- Pre-training multimodal language models for table understanding
- Fine-tuning models for specific table-based tasks
- Evaluating numerical reasoning capabilities in scientific contexts
- Benchmarking table understanding and reasoning systems
#### Table Question Answering (TQA)
*Figure 4: Example of a TQA task showing the question, reasoning steps, and answer.*
#### Table Fact Verification (TFV)
*Figure 5: Example of a TFV task showing the statement, verification process, and conclusion.*
## Citation
If you found this repository or paper is helpful to you, please cite our paper.
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