Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
json
Sub-tasks:
multiple-choice-qa
Languages:
Bengali
Size:
10K - 100K
ArXiv:
License:
Add link to Github repository and specify license (#1)
Browse files- Add link to Github repository and specify license (3b7ea4d5e103916c12ee943f22155bd086df7da5)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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---
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language:
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- bn
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license:
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-
task_categories:
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- question-answering
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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task_ids:
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- multiple-choice-qa
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dataset_info:
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'0': '0'
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'1': '1'
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---
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## Dataset Summary
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This is the translated version of the [PIQA](https://huggingface.co/datasets/ybisk/piqa) LLM evaluation dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Translation with LLM-based rewriting. PIQA introduces the task of physical commonsense reasoning and provides a corresponding benchmark for understanding physical interactions in everyday situations. It focuses on atypical solutions to practical problems, inspired by instructional guides from instructables.com, and aims to tackle one of the major challenges in AI: understanding and reasoning about physical commonsense knowledge.
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## Dataset Structure
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### Data Instances
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.11187},
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}
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```
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---
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language:
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- bn
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license: mit
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multilinguality:
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- monolingual
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size_categories:
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- 10K<n<100K
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task_categories:
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- question-answering
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task_ids:
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- multiple-choice-qa
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dataset_info:
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'0': '0'
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'1': '1'
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---
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+
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## Dataset Summary
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This is the translated version of the [PIQA](https://huggingface.co/datasets/ybisk/piqa) LLM evaluation dataset. The dataset was translated using a new method called Expressive Semantic Translation (EST), which combines Google Translation with LLM-based rewriting. PIQA introduces the task of physical commonsense reasoning and provides a corresponding benchmark for understanding physical interactions in everyday situations. It focuses on atypical solutions to practical problems, inspired by instructional guides from instructables.com, and aims to tackle one of the major challenges in AI: understanding and reasoning about physical commonsense knowledge.
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Code: [https://github.com/hishab-nlp/lm-evaluation-harness](https://github.com/hishab-nlp/lm-evaluation-harness)
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## Dataset Structure
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### Data Instances
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2502.11187},
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}
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```
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