Below Initial User Prompt optimized using Prompt Engineering GPT
Initial User Prompt
Act as a machine learning expert practitioner. I am working on improving the dataset improvements for a text classification problem with more than 100 targets. Some of the targets don't have enough data samples for the model to learn from. Think step by step and provide techniques to explore as alternatives. For examples creating synthetic data using LLM is one such alternative. Provide the outputs in bulleted list.
Revised User Prompt
Assume the role of a machine learning expert practitioner. My project involves enhancing dataset quality for a text classification challenge, targeting over 100 categories. Some categories suffer from insufficient data samples, impacting the model's learning efficiency. Considering this, please outline a step-by-step strategy to address the data imbalance. Focus on methods like data augmentation, including synthetic data generation with LLMs, and consider other innovative techniques suitable for text data. Present your recommendations as a bulleted list, detailing each approach's advantages and potential limitations.
System Generated
When addressing the issue of insufficient data samples for certain targets in a text classification problem with more than 100 targets, it's crucial to adopt a strategic approach to dataset improvement. Here are several techniques to consider, starting with creating synthetic data using large language models (LLMs):
Synthetic Data Generation with LLMs:
Use LLMs to generate additional examples for underrepresented classes. This involves providing the model with a prompt that describes the desired output and ensuring that the synthetic data is diverse and relevant.
It's important to closely monitor the quality of generated text to avoid introducing biases or inaccuracies.
Data Augmentation:
Paraphrasing: Use natural language processing tools to paraphrase existing examples, creating new, varied instances of the same class.
Back-translation: Translate the text to another language and then back to the original language. This can introduce syntactic diversity while maintaining semantic meaning.
Text injection or deletion: Add or remove relevant information from existing samples to create new variations, focusing on maintaining the label's integrity.
Transfer Learning:
Pretrain on a larger, related dataset and fine-tune on your specific, smaller dataset. This leverages learned representations that can generalize well to your task, even with fewer examples for some targets.
Few-shot Learning Techniques:
Explore few-shot learning models that are designed to learn from a small number of examples. Techniques like model-agnostic meta-learning (MAML) or prototypical networks can be effective.
Active Learning:
Employ an active learning strategy to iteratively select the most informative samples for manual annotation. This can be particularly effective in gradually improving the representation of underrepresented classes in your dataset.
Class-weight Adjustment:
Adjust the loss function to give higher weight to underrepresented classes. This can help in compensating for the imbalance and encourage the model to pay more attention to these classes.
Oversampling and Undersampling:
Oversampling: Duplicate samples from underrepresented classes or use techniques like SMOTE (Synthetic Minority Over-sampling Technique) for text data to increase their presence.
Undersampling: Reduce the number of instances from overrepresented classes to balance the dataset. However, this might lead to loss of valuable information.
External Datasets:
Look for external datasets that can complement your existing data, especially for underrepresented classes. Ensure the external data is compatible in terms of quality, style, and domain.
Semi-supervised Learning:
Use unlabeled data to improve learning. Techniques like self-training, where the model's own predictions on unlabeled data are used to retrain it, can help leverage the abundance of unlabeled text.
Each of these techniques has its strengths and considerations. For example, while synthetic data generation with LLMs can quickly increase your dataset size, it's essential to ensure the synthetic data's diversity and accuracy. Similarly, data augmentation methods can introduce variability but must be implemented carefully to avoid altering the meaning of the text in a way that misleads the model. It's often effective to combine several of these strategies, carefully monitoring model performance and data quality throughout the process.
Below Initial User Prompt optimized using Prompt Engineering GPT
Initial User Prompt
Act as a machine learning expert practitioner. I am working on improving the dataset improvements for a text classification problem with more than 100 targets. Some of the targets don't have enough data samples for the model to learn from. Think step by step and provide techniques to explore as alternatives. For examples creating synthetic data using LLM is one such alternative. Provide the outputs in bulleted list.
Revised User Prompt
Assume the role of a machine learning expert practitioner. My project involves enhancing dataset quality for a text classification challenge, targeting over 100 categories. Some categories suffer from insufficient data samples, impacting the model's learning efficiency. Considering this, please outline a step-by-step strategy to address the data imbalance. Focus on methods like data augmentation, including synthetic data generation with LLMs, and consider other innovative techniques suitable for text data. Present your recommendations as a bulleted list, detailing each approach's advantages and potential limitations.
System Generated
When addressing the issue of insufficient data samples for certain targets in a text classification problem with more than 100 targets, it's crucial to adopt a strategic approach to dataset improvement. Here are several techniques to consider, starting with creating synthetic data using large language models (LLMs):
Synthetic Data Generation with LLMs:
Data Augmentation:
Transfer Learning:
Few-shot Learning Techniques:
Active Learning:
Class-weight Adjustment:
Oversampling and Undersampling:
External Datasets:
Semi-supervised Learning:
Each of these techniques has its strengths and considerations. For example, while synthetic data generation with LLMs can quickly increase your dataset size, it's essential to ensure the synthetic data's diversity and accuracy. Similarly, data augmentation methods can introduce variability but must be implemented carefully to avoid altering the meaning of the text in a way that misleads the model. It's often effective to combine several of these strategies, carefully monitoring model performance and data quality throughout the process.