Data Augmentation Techniques for Large-Scale LLM Training

Welcome to the lesson on Data Augmentation for LLM Training. In this lesson, we will explore how data augmentation can enhance the training of large-scale language models (LLMs). Data augmentation involves creating new data samples from existing ones, which can help improve model performance and generalization. By the end of this lesson, you will understand how to apply various data augmentation techniques to your datasets.

Before we dive into data augmentation, let's briefly recall the importance of having clean and well-prepared data. In previous lessons, we discussed techniques for efficient data storage, deduplication, and filtering. These steps ensure that your dataset is free from duplicates, non-English content, and toxicity, which is crucial for effective augmentation. Remember, clean data is the foundation for successful data augmentation.

One common data augmentation technique is synonym replacement, where words in a sentence are replaced with their synonyms. This can help create diverse training samples. We will use the WordNetAugmenter from the textattack library to perform synonym replacement.

First, let's import the necessary library and create a sample text:

from textattack.augmentation import WordNetAugmenter

# Sample text
txt = "The cheerful child played in the sunny park."

Next, we create an instance of WordNetAugmenter and use it to augment the text:

# Synonym Replacement using WordNet
wordnet_aug = WordNetAugmenter()
synonym_augmented = wordnet_aug.augment(txt)

In this code, WordNetAugmenter is used to replace words in the text with their synonyms. The augment method generates a new version of the text with synonyms. Let's see the output:

print("WordNet Synonym Replacement:", synonym_augmented)

Example output:

"The blithe child frolicked in the sunny park."

Easy Data Augmentation (EDA) includes several techniques like synonym replacement, random insertion, and more. These techniques help create diverse training samples with minimal effort. We will use the EasyDataAugmenter class to demonstrate EDA.

First, import the necessary library and create a sample text:

from textattack.augmentation import EasyDataAugmenter

# Sample text
txt = "The cheerful child played in the sunny park."

Now, create an instance of EasyDataAugmenter and use it to augment the text:

# Easy Data Augmentation (EDA)
eda_aug = EasyDataAugmenter()
eda_augmented = eda_aug.augment(txt)

The EasyDataAugmenter applies various EDA techniques to the text. The augment method generates a new version of the text with these techniques. Let's see the output:

print("Easy Data Augmentation:", eda_augmented)

Example output:

"The cheerful child played in the sunny park joyfully."