Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (2306.15895v2)

Abstract: LLMs have been recently leveraged as training data generators for various NLP tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g., specifying attributes like length and style), which have the potential to yield diverse and attributed generated data. Our investigation focuses on datasets with high cardinality and diverse domains, wherein we demonstrate that attributed prompts outperform simple class-conditional prompts in terms of the resulting model's performance. Additionally, we present a comprehensive empirical study on data generation encompassing vital aspects like bias, diversity, and efficiency, and highlight three key observations: firstly, synthetic datasets generated by simple prompts exhibit significant biases, such as regional bias; secondly, attribute diversity plays a pivotal role in enhancing model performance; lastly, attributed prompts achieve the performance of simple class-conditional prompts while utilizing only 5\% of the querying cost of ChatGPT associated with the latter. The data and code are available on \url{https://github.com/yueyu1030/AttrPrompt}.

• The paper introduces AttrPrompt, an attributed prompt method that significantly enhances data diversity and reduces regional bias compared to conventional approaches.
• The study empirically validates AttrPrompt across high-cardinality datasets, achieving superior model performance at only 5% of the querying cost of traditional methods.
• The paper demonstrates that integrating attributed synthetic data improves long-tail and multi-label classification performance while optimizing budget efficiency.

Insights into "LLM as Attributed Training Data Generator: A Tale of Diversity and Bias"

The paper "LLM as Attributed Training Data Generator: A Tale of Diversity and Bias" offers an in-depth investigation into the generation of synthetic training data using LLMs with attributed prompts. The work addresses a significant issue in the current methodology of using LLM-generated data, which primarily relies on simple class-conditional prompts. These methods often result in a lack of diversity and the perpetuation of biases inherent to the LLMs. The authors propose a solution involving diversely attributed prompts, demonstrating that this approach can yield results that surpass class-conditional prompts in multiple facets.

Key Contributions and Findings:

1. Attributed vs. Class-Conditional Prompts: The authors challenge the conventional use of class-conditional prompts—referred to as SimPrompt—which has shown both a significant regional bias and limited diversity in data generation. They introduce AttrPrompt, a method leveraging attributed prompts that incorporate various attributes such as length and style, tailored for different classes. This method not only enhances the diversity of the generated dataset but also markedly reduces biases.
2. Empirical Validation: Through comprehensive experiments across high cardinality datasets and diverse domains, AttrPrompt exhibits improved model performance and efficacy over SimPrompt. Particularly, models trained with datasets generated using attributed prompts consume only 5% of the querying costs associated with SimPrompt while achieving equivalent or superior performance metrics.
3. Bias and Diversity Analysis: A pivotal aspect of the paper is the exploration of dataset biases and diversity metrics. Notably, datasets generated with SimPrompt exhibited pronounced biases toward certain regions. AttrPrompt managed to mitigate these biases and foster a more balanced attribute representation, as validated through both manual annotations and trained attribute classifiers.
4. Performance Implications: The authors empirically show that models trained on data generated via AttrPrompt outperform those trained on datasets generated through SimPrompt, especially in terms of diversity and the handling of long-tail class issues. Additionally, augmenting existing datasets with attributed generated data yields consistent performance improvements.
5. Cost Efficiency: AttrPrompt demonstrates superior budget efficiency, highlighting significant cost reductions due to decreased query frequencies without compromising on data quality or model performance. This optimization is a notable step toward more practical applications where budget constraints are a concern.
6. Extension to Multi-Label Classification: The paper ventures into the field of multi-label classification, serving as a pioneering attempt to leverage LLM-generated training data in this context. AttrPrompt again showcases enhanced performance across various multi-label evaluation metrics compared to its counterparts, setting a foundation for future research endeavors in similar domains.

Implications and Future Directions:

The discussion presented by the authors on attributed data generation opens pathways for significant advancements in the field of AI, particularly in the refinement of synthetic data generation techniques. The implications of this research are multifaceted:

• Increased Accessibility: By decreasing the costs associated with synthetic data generation, AttrPrompt may democratize access to quality training datasets, especially in resource-constrained environments.
• Bias Reduction: The framework offers a promising avenue for tackling embedded biases in AI systems, a critical concern in the deployment of fair and reliable machine learning applications.
• Diverse Application Potential: While focused on text classification, the concept of attributed prompts holds potential for broader application across different modalities and tasks, encouraging future exploration in domains such as image and audio processing.

In conclusion, "LLM as Attributed Training Data Generator: A Tale of Diversity and Bias" advocates a methodologically sound, empirically validated approach to training data generation. It not only enhances model performance but also addresses practical concerns around bias and cost-efficiency. As LLMs evolve, so too does the potential for innovations such as AttrPrompt to reshape the landscape of artificial intelligence research and deployment.