Datasets:
File size: 3,939 Bytes
8e29757 eb8884a b57dca1 4958daf 77de85b 91919c4 8e29757 b41a894 c8e3328 185d9ef c8e3328 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
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}
}
``` |