metadata

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-API is created using MapQaTor.

Usage

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}
}