Synthetic Data Generation Using Large Language Models: Advances in Text and Code
Project Overview
Generative AI, especially through large language models (LLMs), is revolutionizing education by facilitating the generation of synthetic data for diverse applications, such as creating training examples for classification and programming tasks. This innovation addresses issues of data scarcity and reduces annotation costs, making educational practices more efficient and scalable. However, the implementation of generative AI also brings challenges, including concerns over data quality, inherent biases, and the necessity for robust evaluation metrics to assess effectiveness. The document emphasizes the dual nature of these technologies, showcasing their potential to enhance educational outcomes while also underscoring the importance of implementing safeguards to ensure the reliability and fairness of the generated data. By striking a balance between leveraging the benefits of generative AI and addressing its challenges, educators can harness these tools to improve teaching and learning experiences.
Key Applications
Synthetic data generation using LLMs for training classifiers and code models
Context: Educational settings focused on Natural Language Processing (NLP) and programming tasks, targeting students and researchers in computer science.
Implementation: LLMs are prompted to generate text and code data, with techniques like prompt-based generation, retrieval-augmented generation, and iterative refinement.
Outcomes: Significant improvements in model performance, especially in low-resource scenarios, with reported accuracy increases of 3-26% in various classification tasks.
Challenges: Challenges include ensuring factual accuracy, managing biases in generated data, and addressing potential distribution shifts compared to real data.
Implementation Barriers
Quality Assurance
Ensuring the factual correctness of synthetic data generated by LLMs, as they are prone to hallucinations, leading to incorrect information or code.
Proposed Solutions: Integrating retrieval techniques to ground text generation in real facts and employing execution feedback for validating code correctness.
Distribution Shift
Synthetic data may not perfectly follow the distribution of real data, leading to models overfitting to synthetic patterns and failing in real-world applications.
Proposed Solutions: Mixing real data with synthetic data to maintain a core of realistic examples while using synthetic data for augmentation.
Bias Amplification
Synthetic data can inadvertently reflect and amplify biases present in the LLM's training data.
Proposed Solutions: Employing careful prompt design and post-hoc balancing of synthetic datasets to ensure diverse representation.
Project Team
Mihai Nadas
Researcher
Laura Diosan
Researcher
Andreea Tomescu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Mihai Nadas, Laura Diosan, Andreea Tomescu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai