CoolFace
Datasetpublic

MapEval/MapEval-API

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"… See the full description on the dataset page: https://huggingface.co/datasets/MapEval/MapEval-API.

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
2likes70downloads
Dataset Card

MapEval-API

MapEval-API is created using MapQaTor.

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

ModelOverallPlace InfoNearbyRoutingTripUnanswerable
Claude-3.5-Sonnet64.0068.7555.4265.1571.6455.00
GPT-4-Turbo53.6762.5050.6060.6150.7525.00
GPT-4o48.6759.3840.9650.0056.7215.00
Gemini-1.5-Pro43.3365.6330.1240.9134.3365.00
Gemini-1.5-Flash41.6751.5638.5546.9734.3330.00
GPT-3.5-Turbo27.3339.0622.8933.3319.4015.00
GPT-4o-mini23.0028.1314.4613.6443.285.00
Llama-3.2-90B39.6754.6937.3539.3935.8215.00
Llama-3.1-70B37.6753.1332.5342.4231.3415.00
Mixtral-8x7B27.6732.8118.0727.2738.8115.00
Gemma-2.0-9B27.0035.9414.4628.7926.8745.00
Comparison between ReAct and Chameleon with GPT-3.5-Turbo
ModelOverallPlace InfoNearbyRoutingTripUnanswerable
ReAct27.3339.0622.8933.3319.4015.00
Chameleon49.3354.6954.2151.5143.2825.00

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}
}