---
license: apache-2.0
language:
- en
size_categories:
- n<1K
task_categories:
- question-answering
- multiple-choice
configs:
- config_name: benchmark
  data_files:
  - split: test
    path: dataset.json
tags:
- geospatial
annotations_creators:
- expert-generated
paperswithcode_id: mapeval-textual
---


# MapEval-Textual

[MapEval](https://arxiv.org/abs/2501.00316)-Textual is created using [MapQaTor](https://arxiv.org/abs/2412.21015).

## Usage

```python
from datasets import load_dataset

# Load dataset
ds = load_dataset("MapEval/MapEval-Textual", name="benchmark")

# Generate better prompts
for item in ds["test"]:
    # Start with a clear task description
    prompt = (
        "You are a highly intelligent assistant. "
        "Based on the given context, answer the multiple-choice question by selecting the correct option.\n\n"
        "Context:\n" + item["context"] + "\n\n"
        "Question:\n" + item["question"] + "\n\n"
        "Options:\n"
    )
    
    # List the options more clearly
    for i, option in enumerate(item["options"], start=1):
        prompt += f"{i}. {option}\n"
    
    # Add a concluding sentence to encourage selection of the answer
    prompt += "\nSelect the best option by choosing its number."

    # Use the prompt as needed
    print(prompt)  # Replace with your processing logic
```

## Leaderboard

| Model                    | Overall | Place Info | Nearby | Routing | Trip   | Unanswerable |
|--------------------------|:---------:|:------------:|:--------:|:---------:|:--------:|:--------------:|
| Claude-3.5-Sonnet        | **66.33**   | **73.44**      | 73.49  | **75.76**   | **49.25**  | 40.00        |
| Gemini-1.5-Pro           | **66.33**   | 65.63      | **74.70**  | 69.70   | 47.76  | **85.00**        |
| GPT-4o                   | 63.33   | 64.06      | **74.70**  | 69.70   | **49.25**  | 40.00        |
| GPT-4-Turbo              | 62.33   | 67.19      | 71.08  | 71.21   | 47.76  | 30.00        |
| Gemini-1.5-Flash         | 58.67   | 62.50      | 67.47  | 66.67   | 38.81  | 50.00        |
| GPT-4o-mini              | 51.00   | 46.88      | 63.86  | 57.58   | 40.30  | 25.00        |
| GPT-3.5-Turbo            | 37.67   | 26.56      | 53.01  | 48.48   | 28.36  | 5.00         |
| Llama-3.1-70B            | 61.00   | 70.31      | 67.47  | 69.70   | 40.30  | 45.00        |
| Llama-3.2-90B            | 58.33   | 68.75      | 66.27  | 66.67   | 38.81  | 30.00        |
| Qwen2.5-72B              | 57.00   | 62.50      | 71.08  | 63.64   | 41.79  | 10.00        |
| Qwen2.5-14B              | 53.67   | 57.81      | 71.08  | 59.09   | 32.84  | 20.00        |
| Gemma-2.0-27B            | 49.00   | 39.06      | 71.08  | 59.09   | 31.34  | 15.00        |
| Gemma-2.0-9B             | 47.33   | 50.00      | 50.60  | 59.09   | 34.33  | 30.00        |
| Llama-3.1-8B             | 44.00   | 53.13      | 57.83  | 45.45   | 23.88  | 20.00        |
| Qwen2.5-7B               | 43.33   | 48.44      | 49.40  | 42.42   | 38.81  | 20.00        |
| Mistral-Nemo             | 43.33   | 46.88      | 50.60  | 50.00   | 32.84  | 15.00        |
| Mixtral-8x7B             | 43.00   | 53.13      | 54.22  | 45.45   | 26.87  | 10.00        |
| Phi-3.5-mini             | 37.00   | 40.63      | 48.19  | 46.97   | 20.90  | 0.00         |
| Llama-3.2-3B             | 33.00   | 31.25      | 49.40  | 31.82   | 25.37  | 0.00         |
| Human                    | 86.67   | 92.19      | 90.36  | 81.81   | 88.06  | 65.00        |


## Citation

If you use this dataset, please cite the original paper:

```
@article{dihan2024mapeval,
  title={MapEval: A Map-Based Evaluation of Geo-Spatial Reasoning in Foundation Models},
  author={Dihan, Mahir Labib and Hassan, Md Tanvir and Parvez, Md Tanvir and Hasan, Md Hasebul and Alam, Md Almash and Cheema, Muhammad Aamir and Ali, Mohammed Eunus and Parvez, Md Rizwan},
  journal={arXiv preprint arXiv:2501.00316},
  year={2024}
}
```