StudyAbroadGPT Dataset

The **StudyAbroadGPT-Dataset** is a collection of conversational data focused on university application requirements for various programs, including MBA, MS in Computer Science, Data Science, and Bachelor of Medicine. The dataset includes interactions between humans asking questions about application processes (e.g., "How do I write a strong SOP for MS in Data Science at MIT?") and an assistant providing detailed responses. Covering prestigious institutions such as MIT, Oxford, Cambridge, and Stanford, this dataset serves as a valuable resource for understanding the informational needs of prospective students applying to study abroad.

## Dataset Structure

The dataset is organized as a list of JSON objects, where each object represents a single conversation. Each conversation contains an array of turns, structured as follows:

- `"from"`: Specifies the speaker, either "human" or "assistant".
- `"value"`: Contains the text of the query or response.

### Example

```json
{
  "conversations": [
    {"from": "human", "value": "What documents do I need for applying to MBA?"},
    {"from": "assistant", "value": "## Introduction\nTo embark on your MBA journey, it's crucial to gather the necessary documents..."}
  ]
}
```


## Data Collection

This dataset was synthetically generated to simulate realistic conversations about study abroad applications. It is designed to reflect common queries and provide detailed, informative responses related to university application requirements.

## Preprocessing

The dataset is provided in its raw form, consisting of unprocessed conversational text. Depending on their specific use case, such as natural language processing (NLP) tasks, users may need to perform additional preprocessing steps like tokenization or stopword removal.

## Usage

You can load the dataset using the Hugging Face Datasets library with the following code:

```python
from datasets import load_dataset

dataset = load_dataset("millat/StudyAbroadGPT-Dataset")
```

### Accessing Conversations

To access and iterate through the conversations, use this example:

```python
for conversation in dataset["train"]:
    for turn in conversation["conversations"]:
        print(f"{turn['from']}: {turn['value']}")
```

## Examples

Here’s a sample conversation excerpt from the dataset:

```json
{
  "conversations": [
    {"from": "human", "value": "What are the GMAT requirements for Oxford?"},
    {"from": "assistant", "value": "## Introduction\nOxford University, one of the world's most prestigious institutions, requires..."}
  ]
}
```

## Use Cases

- **Training Conversational Agents**: Build chatbots to assist with university application queries.
- **Analyzing Trends**: Study application requirements across different programs and institutions.
- **NLP Development**: Create natural language understanding models tailored to educational domains.

## License

This dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).

## Citation

If you use this dataset in your research, please cite it as follows:

```bibtex
@misc{StudyAbroadGPT-Dataset,
  author = {MD MILLAT HOSEN},
  title = {StudyAbroadGPT-Dataset},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/millat/StudyAbroadGPT-Dataset}}
}
```

Introducing paper: A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained Settings