---
language:
- en
- hi
license: cc-by-nc-sa-4.0
size_categories:
- 1K<n<10K
task_categories:
- table-question-answering
- visual-question-answering
- image-text-to-text
tags:
- cricket
configs:
- config_name: default
  data_files:
  - split: test_single
    path: data/test_single-*
  - split: test_multi
    path: data/test_multi-*
dataset_info:
  features:
  - name: id
    dtype: string
  - name: images
    sequence: image
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: category
    dtype: string
  - name: subset
    dtype: string
  splits:
  - name: test_single
    num_bytes: 976385438.0
    num_examples: 2000
  - name: test_multi
    num_bytes: 904538778.0
    num_examples: 997
  download_size: 1573738795
  dataset_size: 1880924216.0
---

# MMCricBench 🏏
**Multimodal Cricket Scorecard Benchmark for VQA**

This repository contains the dataset for the paper [Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs](https://huggingface.co/papers/2508.17334).

MMCricBench evaluates **Large Vision-Language Models (LVLMs)** on **numerical reasoning**, **cross-lingual understanding**, and **multi-image reasoning** over semi-structured cricket scorecard images. It includes English and Hindi scorecards; all questions/answers are in English.

---

## Overview
- **Images:** 1,463 synthetic scorecards (PNG)  
  - 822 single-image scorecards  
  - 641 multi-image scorecards
- **QA pairs:** 1,500 (English)
- **Reasoning categories:**  
  - **C1** – Direct retrieval & simple inference  
  - **C2** – Basic arithmetic & conditional logic  
  - **C3** – Multi-step quantitative reasoning (often across images)

---

## Files / Splits
We provide two evaluation splits:
- `test_single` — single-image questions  
- `test_multi` — multi-image questions

> If you keep a single JSONL (e.g., `test_all.jsonl`), use a **list** for `images` in every row. Single-image rows should have a one-element list. On the Hub, we expose two test splits.

---

## Data Schema
Each row is a JSON object:

| Field      | Type                | Description                                  |
|------------|---------------------|----------------------------------------------|
| `id`       | `string`            | Unique identifier                            |
| `images`   | `list[string]`      | Paths to one or more scorecard images        |
| `question` | `string`            | Question text (English)                      |
| `answer`   | `string`            | Ground-truth answer (canonicalized)          |
| `category` | `string` (`C1/C2/C3`)| Reasoning category                           |
| `subset`*  | `string` (`single/multi`) | Optional convenience field              |

**Example (single-image):**
```json
{"id":"english-single-9","images":["English-apr/single_image/1198246_2innings_with_color1.png"],"question":"Which bowler has conceded the most extras?","answer":"Wahab Riaz","category":"C2","subset":"single"}
```

## Loading & Preview

### Load from the Hub (two-split layout)
```python
from datasets import load_dataset

# Loads: DatasetDict({'test_single': ..., 'test_multi': ...})
ds = load_dataset("DIALab/MMCricBench")
print(ds)

# Peek a single-image example
ex = ds["test_single"][0]
print(ex["id"])
print(ex["question"], "->", ex["answer"])

# Preview images (each example stores a list of PIL images)
from IPython.display import display
for img in ex["images"]:
    display(img)
```

## Baseline Results (from the paper)

Accuracy (%) on MMCricBench by split and language.

| Model             | #Params | Single-EN (Avg) | Single-HI (Avg) | Multi-EN (Avg) | Multi-HI (Avg) |
|-------------------|:------:|:---------------:|:---------------:|:--------------:|:--------------:|
| SmolVLM           | 500M   | 19.2 | 19.0 | 11.8 | 11.6 |
| Qwen2.5VL         | 3B     | 40.2 | 33.3 | 31.2 | 22.0 |
| LLaVA-NeXT        | 7B     | 28.3 | 26.6 | 16.2 | 14.8 |
| mPLUG-DocOwl2     | 8B     | 20.7 | 19.9 | 15.2 | 14.4 |
| Qwen2.5VL         | 7B     | 49.1 | 42.6 | 37.0 | 32.2 |
| InternVL-2        | 8B     | 29.4 | 23.4 | 18.6 | 18.2 |
| Llama-3.2-V       | 11B    | 27.3 | 24.8 | 26.2 | 20.4 |
| **GPT-4o**        | —      | **57.3** | **45.1** | **50.6** | **43.6** |

*Numbers are exact-match accuracy (higher is better). For C1/C2/C3 breakdowns, see Table 3 (single-image) and Table 5 (multi-image) in the paper.* 

## Contact
For questions or issues, please open a discussion on the dataset page or email **Abhirama Subramanyam** at penamakuri.1@iitj.ac.in