Logical Reasoning Improvement Dataset
Enhancing LLM Logical Reasoning Skills with Platypus2 Models
@kaggle.thedevastator_logical_reasoning_improvement_dataset
About this Dataset
Logical Reasoning Improvement Dataset
Logical Reasoning Improvement Dataset
Enhancing LLM Logical Reasoning Skills with Platypus2 Models
By garage-bAInd (From Huggingface) [source]
About this dataset
The garage-bAInd/Open-Platypus dataset is a curated collection of data specifically designed to enhance logical reasoning skills in LLM (Legal Language Model) models. It serves as a training resource for improving the ability of these models to reason logically and provide accurate solutions or answers to various logical reasoning questions.
This dataset, which has been utilized in training the Platypus2 models, consists of multiple datasets that have undergone a meticulous filtering process. Through keyword search and the application of Sentence Transformers technique, questions with a similarity score above 80% have been eliminated, ensuring that only unique and diverse logical reasoning questions are included.
The columns in this dataset include:
input: The input text or question that requires logical reasoning.output: The correct answer or solution to the logical reasoning question.instruction: Additional instructions or guidelines for solving the logical reasoning question.data_source: The source or origin of the logical reasoning question.By utilizing this comprehensive and carefully curated dataset, LLM models can be trained more effectively to improve their logical reasoning capabilities
How to use the dataset
How to Use This Dataset: Logical Reasoning Improvement
Dataset Overview
Columns
The dataset is organized into several columns, each serving a specific purpose:
- input: The input text or question that requires logical reasoning. This column provides the initial statement or problem that needs solving.
- output: The correct answer or solution to the logical reasoning question. This column contains the expected outcome or response.
- instruction: Additional instructions or guidelines for solving the logical reasoning question. This column provides any specific guidance or steps required to arrive at the correct answer.
- data_source: The source or origin of the logical reasoning question. This column specifies where the question was obtained from.
Usage Guidelines
To make effective use of this dataset, follow these guidelines:
- Familiarize Yourself: Take time to understand and familiarize yourself with each entry in the dataset.
- Analyze Inputs: Carefully read and analyze each input text/question provided in the input column.
- Solve Using Logic: Apply logical thinking and reasoning strategies based on your understanding of each problem.
- Confirm Answers: Compare your solutions with those provided in the output column to check their accuracy.
- Follow Instructions: Always consider any additional instructions given in the instruction column while solving a problem.
- Explore Data Sources: Utilize information from different data sources mentioned in the data_source column if needed.
Remember, practice makes perfect! Continuously work through the dataset to improve your logical reasoning skills.
Please note that this guide aims to help you utilize the dataset effectively. It does not provide direct solutions or explanations for specific entries in the dataset.
Contributing and Feedback
We believe in continuous improvement! If you have any feedback or would like to contribute additional logical reasoning questions, please feel free to do so. Together, we can enhance this dataset further and promote logical reasoning skills across LLM models.
Let's get started and embark on a journey of logical reasoning improvement with this curated dataset!
Research Ideas
- Training and evaluating logical reasoning models: The dataset can be used to train and evaluate logical reasoning models, such as Platypus2, to enhance their performance in solving a variety of logical reasoning questions.
- Benchmarking logical reasoning algorithms: Researchers and developers can use this dataset as a benchmark for testing and comparing different logical reasoning algorithms and techniques.
- Creating educational resources: The dataset can be utilized to create educational resources or platforms that focus on improving logical reasoning skills. It can serve as a valuable source of practice questions for learners looking to enhance their abilities in this area
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: train.csv
| Column name | Description |
|---|---|
| input | The input text or question that requires logical reasoning. (Text) |
| output | The correct answer or solution to the logical reasoning question. (Text) |
| instruction | Additional instructions or guidelines for solving the logical reasoning question. (Text) |
| data_source | The source or origin of the logical reasoning question. (Text) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit garage-bAInd (From Huggingface).