spatialft/LFM2-350M-StepGame-GGUF
Text Generation • 0.4B • Updated • 9
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We have three files in the dataset (k is the number of maximum hops required to answer the question in the dataset):
train.json: The "TrainVersion" is utilised in the baseline models presented in our paper. We use k=1,2,3,4,5 for training without noise.valid.json: The "TrainVersion" is utilised in the baseline models presented in our paper. We use k=1,2,3,4,5 for validation without noise.test.json: The "TrainVersion" is utilised in the baseline models presented in our paper. We use k=1,2,3,4,5,6,7,8,9,10 for testing with noise.In StepGame dataset, we have 4 features:
Here is an example of a sample from the dataset:
{
"story": [
"S is above J and to the left of J.",
"J is diagonally above B to the right at a 45 degree.",
"V is there and A is at the 2 position of a clock face."
],
"question": "What is the relation of the agent B to the agent J?",
"label": "lower-left",
"k_hop": "1"
}
This dataset is sourced from the paper "StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts" by Zhengxiang Shi, Qiang Zhang, and Aldo Lipani. The dataset is available at https://github.com/ZhengxiangShi/StepGame.
@inproceedings{stepGame2022shi,
title={StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts},
author={Shi, Zhengxiang and Zhang, Qiang and Lipani, Aldo},
volume={36},
url={https://ojs.aaai.org/index.php/AAAI/article/view/21383},
DOI={10.1609/aaai.v36i10.21383},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2022},
month={Jun.},
pages={11321-11329}
}