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Alpaca GPT-4 Instruction-Following Reasoning Data
Data Science and Analytics
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About
This collection contains 52,000 instruction-following samples generated by the GPT-4 model in English. The data uses the same foundational prompts as the original Alpaca dataset, enabling researchers to explore novel strategies within natural language processing, specifically focusing on instruction-following reasoning. This resource offers ample variety for experimenting with models designed to excel at complex instruction following tasks. It is an invaluable resource for rapidly iterating experiments and advancing artificial intelligence techniques related to logical reasoning problems.
Columns
The dataset includes four critical columns, which collaborate together integrally to provide essential data for model evaluation:
- instruction: This field supplies the prompt initially given to the GPT-4 language model. This statement must be interpreted correctly by an AI model to successfully complete the specified task. (This column contains 52,002 unique and 100% valid values).
- input: This text comprises pre-generated data that helps an AI model contextualise and make sense of the instructions. (Approximately 60% of the records in this column are missing input values).
- output: This indicates the specific result that should be returned after the AI model has correctly interpreted the instructions. (This column contains 51,749 unique and 100% valid values).
- text: This field holds the full text generated by GPT-4, providing deeper insight into how the final output results were derived through the handling of the instruction and input. (This column contains 52,002 unique and 100% valid values).
Distribution
The data is provided in a single CSV file,
train.csv, which has a size of approximately 88.26 MB. The file contains 52,000 instruction-following samples presented in English across four columns.Usage
This data is ideally suited for a variety of advanced AI and NLP applications, including:
- Refining specific model components, such as predicting outputs or analysing lengthy textual conversations.
- Training and evaluating end-to-end instruction-following approaches.
- Developing more powerful instruction processing models driven by algorithms for natural language understanding and reasoning.
- Training intelligent conversational agents with advanced instruction-following reasoning capabilities.
- Establishing online platforms for academic, business, or other organisations to construct affordable, large-scale auto-grading systems for assessing staff instruction-following skills.
Coverage
The scope of this dataset is centred on high-performance natural language processing (NLP) and models requiring advanced instruction-following reasoning. The collection consists of 52,000 instruction-following data points generated synthetically by the GPT-4 model.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
This dataset is an invaluable resource for:
- Researchers: To explore new strategies for instruction-following reasoning and logical problem solving.
- AI Developers: To rapidly iterate experiments and test models that must excel at complex instruction following tasks.
- Organisations (Academic/Business): To help construct cost-effective auto-grading systems for evaluating instruction-following skills among personnel.
Dataset Name Suggestions
- Alpaca GPT-4 Instruction-Following Reasoning Data
- High-Performance NLP Instruction Set (52K)
- GPT-4 Logical Reasoning Samples
- Instruction-Following AI Training Corpus
Attributes
Original Data Source: Alpaca GPT-4 Instruction-Following Reasoning Data
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