license:openrail region:us
---
license: openrail
---
1. Dataset Description:

Purpose: The dataset aims to train models to solve math word problems, providing step-by-step calculations with expected output.

2. Data Collection and Processing:

Source: GPT 4 
Processing: The dataset is structured with math problems given as "instruction" and their step-by-step solutions as "output".

3. Data Attributes:

instruction (String): A textual representation of the math word problem.
output (String): Detailed step-by-step calculations leading to the solution. It appears that placeholders like <<>> are used to indicate calculations, and "####" is used to present the final answer.

4. Sample Data Point:

{
    "instruction": "Rohit is saving money for a new wallet which costs $100. Rohit has only half of the money he needs. His parents decided to give him $15 for that purpose, and his grandparents twice as much as his parents. How much more money does Rohit need to buy the wallet?",
    "output": "Rohit has only 100 * 0.5 = $<<100*0.5=50>>50.\nRohit's grandparents gave him 15 * 2 = $<<15*2=30>>30.\nIn total, Rohit needs 100 - 50 - 30 - 15 = $<<100-50-30-15=5>>5 more.\n#### 5"
  }
  
5. Potential Uses:

Training models to comprehend and solve math word problems.
Evaluating models' ability to perform mathematical operations based on textual context.

6. Potential Biases, Ethical Considerations, and Limitations:

Scope: The provided samples seem to revolve around basic arithmetic. If this pattern holds for the entire dataset, it might not cover more complex math problems or higher-level mathematics.
Simplicity: Some real-world math problems might require more advanced problem-solving techniques than simple arithmetic.

7. Dataset Maintenance and Updates:

will try to keep in loop



offers several merits for LLMs:

1. Structured Problem Solving:

Merit: The dataset encourages structured problem-solving. Each solution is broken down into steps, reinforcing the idea that problems often need a sequential approach.
Learning: Transformers excel at learning sequences and patterns. By observing structured step-by-step solutions, they can learn the logical progression of tackling mathematical problems.

2. Varied Expression:

Merit: The dataset offers multiple ways to solve the same problem, emphasizing that there's often more than one way to approach a solution.
Learning: This can enhance the generalization capabilities of transformers. They can learn that while different paths may be taken, they can still lead to the same solution. This reduces overfitting to a singular method of problem-solving.

3. Explicit Arithmetic Computations:

Merit: The use of placeholders like <<>> clearly indicates where arithmetic operations occur, making it explicit what computations are being performed.
Learning: Transformers can utilize such explicit markers to better identify and learn arithmetic patterns, focusing on these sections for numeric computations.

4. Clear Answer Indication:

Merit: The "####" tag provides a clear indication of the final answer, differentiating it from the intermediate steps.
Learning: This can help the model discern between intermediate computations and final outcomes. When queried, the model can then prioritize presenting such clear answers.

5. Contextual Comprehension:

Merit: Math problems are embedded in worded instructions, demanding not just mathematical ability but also linguistic comprehension.
Learning: Transformers can fine-tune their contextual understanding by discerning relevant information from word problems, integrating their language model training with arithmetic capabilities.



In essence, the dataset's design provides a comprehensive approach to training transformers on mathematical problem-solving, offering both linguistic comprehension and arithmetic execution.
