Read more about NLarge
Read more about NLarge
DA is a widely used technique in machine learning to enhance the diversity and robustness of training datasets. By artificially expanding the dataset, DA helps improve the generalization capabilities of models, particularly in scenarios where labeled data is scarce or expensive to obtain.In the context of Natural Language Processing (NLP), DA poses unique challenges due to the complexity and variability of human language.
Traditional DA methods in NLP, such as synonym replacement, random insertion, and back-translation, have shown limited effectiveness in generating diverse and meaningful variations of text data. These methods often fail to capture the nuanced semantics and contextual dependencies inherent in natural language, leading to suboptimal improvements in model performance.
Recent advancements in deep learning, particularly the development of Large Language Models (LLMs) like GPT-2, GPT-3, and T5, have opened new avenues for DA in NLP. These models, pre-trained on vast corpora of text data, possess a remarkable ability to generate coherent and contextually relevant text. Leveraging LLMs for DA involves generating synthetic data samples by providing prompts based on existing training examples.
DA has been a widely researched area in the field of Natural Language Processing (NLP) due to its potential to enhance the diversity and robustness of training datasets. In the context of sentiment analysis, DA techniques are particularly valuable as they help improve the generalization capabilities of models, especially when labeled data is scarce.
Rule based methods like random replacement are quick to implement but lack the generalisability to different corpus. These methods aim to generate new training samples by making small perturbations to the existing data, thereby increasing the size of the training set and improving the generalization capabilities of sentiment analysis models.
Interpolation methods such as synonym replacement has also been developedwhere words in a sentence are replaced with their synonyms. This method has been shown to improve model performance by introducing lexical diversity. However, it often fails to capture the nuanced semantics and contextual dependencies inherent in natural language, leading to suboptimal improvements in sentiment analysis tasks.
Leading us to the current state of the art, the use of LLMs for data augmentation has shown promising results in improving the performance of NLP models. By leveraging the generative capabilities of LLMs we are able to reduce the amount of noise introduced and thus generate a higher quality dataset. Most of the research has been focused on NER tasks, and we aim to explore the feasibility of using LLMs for DA in sentiment analysis tasks to ascertain the effectiveness of this approach.
The benefits of LLM DA will still continue to provide superior performance in sentiment analysis tasks over pre-LLM DA methods.
NLarge is a Python library designed to enhance NLP model performance through advanced data augmentation (DA) techniques tailored for sentiment analysis. Our library incorporates both traditional methods (like random and synonym substitutions) and sophisticated techniques using large language models (LLMs) to generate diverse, contextually relevant samples. By increasing dataset variability, NLarge empowers models to generalize better to unseen data.
Our library offers three main types of data augmentation methods, each contributing uniquely to improved model performance:
Random substitution replaces words in the dataset with randomly selected words from the vocabulary. This technique introduces sentence structure variability, aiding models in learning general patterns and preventing overfitting.
Synonym substitution swaps words for their synonyms, allowing models to recognize semantic similarity between different phrasings. This type of augmentation proved effective in creating meaningful variations while maintaining sentence coherence.
Leveraging large language models (LLMs), we employed techniques like paraphrasing and summarization to generate high-quality samples. These methods provide models with contextually diverse data, which enhances accuracy, particularly at extreme augmentation levels. Our studies revealed that summarization approaches produced fewer out-of-vocabulary words, further improving model performance.
Our experiments confirmed that data augmentation, especially at higher levels, enhances NLP model performance for sentiment analysis. Models trained with 20% or more augmented data consistently outperformed those with lower or no augmentation. Traditional DA methods improved model accuracy, while LLM-based approaches offered additional performance gains, especially in extreme cases (200% augmentation), where the RNN model achieved over 90% accuracy. For researchers and practitioners, NLarge provides a flexible toolkit to explore, apply, and optimize DA strategies, helping to advance NLP model robustness and generalization.
© 2024 NLarge. All rights reserved
Created as part of Nanyang Technological University: SC4001 Neural Network & Deep Learning