README.md

metadata

language:
  - bn
license: mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
task_categories:
  - question-answering
task_ids:
  - multiple-choice-qa
dataset_info:
  features:
    - name: goal
      dtype: string
    - name: sol1
      dtype: string
    - name: sol2
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': '0'
            '1': '1'

Dataset Summary

This is the translated version of the 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.

Code: https://github.com/hishab-nlp/lm-evaluation-harness

Dataset Structure

Data Instances

An example looks like this:

{
  "goal": "ডিম সেদ্ধ করার পদ্ধতি।",
  "sol1": "ডিমগুলো একটি পাত্রে রাখুন আর এক ইঞ্চি জল দিয়ে ঢাকনা দিন, মাঝারি আঁচে ফুটিয়ে নিন, তারপর ঢাকনা বন্ধ রেখে গ্যাস বন্ধ করে 8 থেকে 10 মিনিট রেখে দিন।",
  "sol2": "ডিমগুলো পাত্রে রাখুন আর সেখানে এক ইঞ্চি ঠাণ্ডা জল দিয়ে ঢাকনা দিন, মাঝারি আঁচে ফুটিয়ে নিন, তারপর ঢাকনা বন্ধ রেখে গ্যাস বন্ধ করে 8 থেকে 10 মিনিট রেখে দিন।",
  "label": 1
}

Data Fields

List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.

• goal: the question which requires physical commonsense to be answered correctly
• sol1: the first solution
• sol2: the second solution
• label: the correct solution. 0 refers to sol1 and 1 refers to sol2

Data Split

Split	Number
Train	15339
Validation	1838

Licensing Information

The dataset is licensed under the MIT License.

Citation

@misc{nahin2025titullmsfamilybanglallms,
      title={TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking}, 
      author={Shahriar Kabir Nahin and Rabindra Nath Nandi and Sagor Sarker and Quazi Sarwar Muhtaseem and Md Kowsher and Apu Chandraw Shill and Md Ibrahim and Mehadi Hasan Menon and Tareq Al Muntasir and Firoj Alam},
      year={2025},
      eprint={2502.11187},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.11187}, 
}