AI Question Answering Data

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

Usage

• Training and Testing: Utilise train.csv to train question-answering models or algorithms, and test.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

License

Who Can Use It

• 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