AI Question Answering Data
Data Science and Analytics
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About
This dataset provides essential information for entries related to question answering tasks using AI models. It is designed to offer valuable insights for researchers and practitioners, enabling them to effectively train and rigorously evaluate their machine learning models. The dataset serves as a valuable resource for building and assessing question-answering systems. It is available free of charge.
Columns
- instruction: Contains the specific instructions given to a model to generate a response.
- responses: Includes the responses generated by the model based on the given instructions.
- next_response: Provides the subsequent response from the model, following a previous response, which facilitates a conversational interaction.
- answer: Lists the correct answer for each question presented in the instruction, acting as a reference for assessing the model's accuracy.
- is_human_response: A boolean column that indicates whether a particular response was created by a human or by a machine learning model, helping to differentiate between the two. Out of nearly 19,300 entries, 254 are human-generated responses, while 18,974 were generated by models.
Distribution
The data files are typically in CSV format, with a dedicated
train.csv
file for training data and a test.csv
file for testing purposes. The training file contains a large number of examples. Specific dates are not included within this dataset description, focusing solely on providing accurate and informative details about its content and purpose. Specific numbers for rows or records are not detailed in the available information.Usage
This dataset is ideal for a variety of applications and use cases:
- Training and Testing: Utilise
train.csv
to train question-answering models or algorithms, andtest.csv
to evaluate their performance on unseen questions. - Machine Learning Model Creation: Develop machine learning models specifically for question-answering by leveraging the instructional components, including instructions, responses, next responses, and human-generated answers, along with their
is_human_response
labels. - Model Performance Evaluation: Assess model performance by comparing predicted responses with actual human-generated answers from the
test.csv
file. - Data Augmentation: Expand existing data by paraphrasing instructions or generating alternative responses within similar contexts.
- Conversational Agents: Build conversational agents or chatbots by utilising the instruction-response pairs for training.
- Language Understanding: Train models to understand language and generate responses based on instructions and previous responses.
- Educational Materials: Develop interactive quizzes or study guides, with models providing instant feedback to students.
- Information Retrieval Systems: Create systems that help users find specific answers from large datasets.
- Customer Support: Train customer support chatbots to provide quick and accurate responses to inquiries.
- Language Generation Research: Develop novel algorithms for generating coherent responses in question-answering scenarios.
- Automatic Summarisation Systems: Train systems to generate concise summaries by understanding main content through question answering.
- Dialogue Systems Evaluation: Use the instruction-response pairs as a benchmark for evaluating dialogue system performance.
- NLP Algorithm Benchmarking: Establish baselines against which other NLP tools and methods can be measured.
Coverage
The dataset's geographic scope is global. There is no specific time range or demographic scope noted within the available details, as specific dates are not included.
License
CC0
Who Can Use It
This dataset is highly suitable for:
- Researchers and Practitioners: To gain insights into question answering tasks using AI models.
- Developers: To train models, create chatbots, and build conversational agents.
- Students: For developing educational materials and enhancing their learning experience through interactive tools.
- Individuals and teams working on Natural Language Processing (NLP) projects.
- Those creating information retrieval systems or customer support solutions.
- Experts in natural language generation (NLG) and automatic summarisation systems.
- Anyone involved in the evaluation of dialogue systems and machine learning model training.
Dataset Name Suggestions
- AI Question Answering Data
- Conversational AI Training Data
- NLP Question-Answering Dataset
- Model Evaluation QA Data
- Dialogue Response Dataset
Attributes
Original Data Source: Question-Answering Training and Testing Data