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OpenBook QA Reasoning Dataset
Education & Learning Analytics
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Free
About
This dataset is designed to promote advanced question-answering research, focusing on a deeper understanding of both subject matter and language. It includes questions that necessitate multi-step reasoning, the use of additional common and commonsense knowledge, and rich text comprehension. Modelled after open book exams, it serves as a challenging benchmark to advance the state-of-the-art in question-answering models and encourages new model architectures capable of handling complex questions and reasoning.
Columns
The dataset, typically found in a CSV file, contains the following columns:
- id: A unique identifier for each question and answer entry.
- question_stem: The main part or stem of the question (String).
- choices: A list of possible answers to choose from for each question (List).
- answerKey: An integer representing the index of the correct answer within the 'choices' list.
Distribution
The dataset is provided in a CSV file format. A sample file will be updated separately to the platform. While specific row counts are not explicitly stated, the columns 'question_stem', 'choices', and 'answerKey' each contain approximately 500 unique values, suggesting the dataset comprises around 500 records.
Usage
This dataset is ideal for various applications and use cases, including:
- Research into advanced question-answering systems.
- Developing models that require multi-step reasoning.
- Building AI systems that can leverage common and commonsense knowledge.
- Enhancing models for rich text comprehension.
- Creating benchmarks for assessing human-like understanding of subjects.
Coverage
The dataset's coverage is global, indicating its applicability and relevance across different regions. It focuses on question-answering tasks and does not specify particular geographic or demographic ranges beyond its global scope. No specific notes on data availability for certain groups or years are provided.
License
CC0
Who Can Use It
This dataset is intended for a range of users, including:
- Researchers and academics working on natural language processing (NLP) and artificial intelligence (AI).
- Data scientists and machine learning engineers developing advanced question-answering models.
- Educators and curriculum developers interested in creating challenging assessment tools.
- Anyone looking to push the boundaries of current AI models in complex reasoning and comprehension tasks.
Dataset Name Suggestions
- OpenBook QA Reasoning Dataset
- Multi-Step Reasoning Questions
- Commonsense QA Benchmark
- Advanced Question-Answering Data
- OpenBook Reasoning Challenge
Attributes
Original Data Source: OpenBookQA (Multi-step Reasoning)
QUALITY