metadata

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-Textual is created using MapQaTor.

Usage

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}
}