| --- |
| license: apache-2.0 |
| task_categories: |
| - question-answering |
| - multiple-choice |
| language: |
| - en |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: benchmark |
| data_files: |
| - split: test |
| path: dataset.json |
| paperswithcode_id: mapeval-api |
| tags: |
| - geospatial |
| --- |
| |
| # MapEval-API |
|
|
| [MapEval](https://arxiv.org/abs/2501.00316)-API is created using [MapQaTor](https://arxiv.org/abs/2412.21015). |
|
|
| # Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load dataset |
| ds = load_dataset("MapEval/MapEval-API", name="benchmark") |
| |
| # Generate better prompts |
| for item in ds["test"]: |
| # Start with a clear task description |
| prompt = ( |
| "You are a highly intelligent assistant. " |
| "Answer the multiple-choice question by selecting the correct option.\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 | **64.00** | **68.75** | **55.42** | **65.15** | **71.64** | 55.00 | |
| | GPT-4-Turbo | 53.67 | 62.50 | 50.60 | 60.61 | 50.75 | 25.00 | |
| | GPT-4o | 48.67 | 59.38 | 40.96 | 50.00 | 56.72 | 15.00 | |
| | Gemini-1.5-Pro | 43.33 | 65.63 | 30.12 | 40.91 | 34.33 | **65.00** | |
| | Gemini-1.5-Flash | 41.67 | 51.56 | 38.55 | 46.97 | 34.33 | 30.00 | |
| | GPT-3.5-Turbo | 27.33 | 39.06 | 22.89 | 33.33 | 19.40 | 15.00 | |
| | GPT-4o-mini | 23.00 | 28.13 | 14.46 | 13.64 | 43.28 | 5.00 | |
| | Llama-3.2-90B | 39.67 | 54.69 | 37.35 | 39.39 | 35.82 | 15.00 | |
| | Llama-3.1-70B | 37.67 | 53.13 | 32.53 | 42.42 | 31.34 | 15.00 | |
| | Mixtral-8x7B | 27.67 | 32.81 | 18.07 | 27.27 | 38.81 | 15.00 | |
| | Gemma-2.0-9B | 27.00 | 35.94 | 14.46 | 28.79 | 26.87 | 45.00 | |
| #### Comparison between ReAct and Chameleon with GPT-3.5-Turbo |
| | Model | Overall | Place Info | Nearby | Routing | Trip | Unanswerable | |
| |----------------------------|:-------:|:----------:|:------:|:-------:|:------:|:------------:| |
| | ReAct | 27.33 | 39.06 | 22.89 | 33.33 | 19.40 | 15.00 | |
| | Chameleon | 49.33 | 54.69 | 54.21 | 51.51 | 43.28 | 25.00 | |
|
|
| ## 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} |
| } |
| ``` |
