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Extraction of Training Data from Fine-tuned Large Language Models Minor Shankar CMUCS24114April 2024Computer Science Department School of Computer Science Carnegie Mellon University Pittsburgh, PA

How to fill out extraction of training data

How to fill out extraction of training data

1. Identify the objective of the training data extraction process
2. Collect relevant data sources for training data
3. Preprocess the data to clean and format it for analysis
4. Annotate the data with labels or tags for classification purposes
5. Split the data into training and testing sets
6. Train a machine learning model using the training data

Who needs extraction of training data?

1. Data scientists
2. Machine learning engineers
3. Researchers in the field of artificial intelligence
4. Companies developing AI-based products
5. Academic institutions conducting research on machine learning

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What is extraction of training data?

Extraction of training data refers to the process of gathering and preparing data that is used for training machine learning models. This data is essential for ensuring that models can learn effectively and provide accurate predictions.

Who is required to file extraction of training data?

Entities or organizations that develop and deploy machine learning models are typically required to file extraction of training data, especially if mandated by regulatory bodies to ensure compliance with data usage policies.

How to fill out extraction of training data?

To fill out an extraction of training data form, one generally needs to provide details regarding the data sources, the types of data extracted, methods used for extraction, and any preprocessing steps taken to ensure data quality.

What is the purpose of extraction of training data?

The purpose of extraction of training data is to compile a high-quality dataset that models can learn from, allowing them to recognize patterns, make predictions, and perform tasks effectively.

What information must be reported on extraction of training data?

The information that must be reported includes data sources, types of data, volume of data, extraction methods, preprocessing details, and compliance with data privacy regulations.