On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey

Original Paper: https://arxiv.org/abs/2406.15126

By: Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang

Abstract:

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models (LLMs) offers a data-centric solution to alleviate the limitations of real-world data with synthetic data generation. However, current investigations into this field lack a unified framework and mostly stay on the surface. Therefore, this paper provides an organization of relevant studies based on a generic workflow of synthetic data generation. By doing so, we highlight the gaps within existing research and outline prospective avenues for future study. This work aims to shepherd the academic and industrial communities towards deeper, more methodical inquiries into the capabilities and applications of LLMs-driven synthetic data generation.

Summary Notes

Introduction

In the rapidly evolving field of deep learning, the quantity and quality of data are paramount. Engineers and data scientists often grapple with the high costs, data scarcity, and privacy concerns associated with obtaining high-quality real-world data. Enter Large Language Models (LLMs) – these powerful tools offer a promising solution by generating synthetic data. But how effective are they? Let's delve into a recent survey that organizes and evaluates the current landscape of LLM-driven synthetic data generation, curation, and evaluation.

Key Methodologies

Data Generation

The process of generating synthetic data using LLMs can be broadly categorized into two main strategies: prompt engineering and multi-step generation.

Prompt Engineering

Prompt engineering is the art of crafting specific instructions to guide the LLMs in generating data that meets desired criteria. This involves:

• Task Specification: Setting a clear context for the LLMs, such as role-playing scenarios or format clarifications.
• Conditional Prompting: Using condition-value pairs to define the attributes and characteristics of the desired data, ensuring diversity and coverage.
• In-Context Learning: Providing exemplars within the prompt to guide the LLMs, leveraging their strong in-context learning capabilities.

Multi-Step Generation

Sometimes, generating complex data in one go is unrealistic due to the limitations in reasoning abilities of LLMs. Multi-step generation decomposes the process into simpler sub-tasks:

• Sample-Wise Decomposition: Breaking down the data into smaller chunks and generating each part sequentially.
• Dataset-Wise Decomposition: Adjusting generation conditions dynamically to ensure the overall dataset grows in the right direction, enhancing both diversity and domain coverage.

Main Findings and Results

The survey highlights several successful implementations of LLM-driven synthetic data generation:

• Improved Data Quality: Techniques like Chain-of-Thought (CoT) prompting significantly enhance the faithfulness of generated data.
• Enhanced Diversity: Conditional prompting with fine-grained attributes leads to more diversified generation, crucial for preventing model overfitting.
• Cost-Effective Annotation: Selective annotation strategies efficiently balance human and LLM efforts, reducing costs while maintaining data quality.

Implications and Potential Applications

The implications of LLM-driven synthetic data generation are vast and transformative:

• Scalable Data Solutions: LLMs enable the creation of large-scale, high-quality datasets with minimal human intervention, accelerating model training and evaluation processes.
• Bias Mitigation: By actively designing what models learn through data manipulation, LLMs can help mitigate inherent biases in human-generated data.
• Domain-Specific Applications: From medical diagnosis to legal document review, the ability to generate high-quality synthetic data tailored to specific domains can revolutionize various industries.

Conclusion

The survey underscores the immense potential of LLMs in addressing the perennial challenges of data quantity and quality in deep learning. By leveraging advanced techniques in prompt engineering and multi-step generation, LLMs can produce synthetic data that is both diverse and faithful, paving the way for more effective and scalable AI solutions. As we continue to refine these methodologies and explore new applications, the synergy between large and small models, coupled with human-model collaboration, will be key to unlocking the full potential of synthetic data generation.

Final Thoughts

The journey of LLM-driven synthetic data generation is just beginning. As we navigate this exciting frontier, the insights and innovations from ongoing research will undoubtedly shape the future of data-centric AI. Engineers and data scientists should keep a keen eye on these developments, as they hold the promise of transforming how we approach data collection, model training, and beyond.