/Synthetic_Data_for_LLM_De-Biasing

This proposal addresses biases in Large Language Models (LLMs) by generating high-quality, low-bias synthetic data using advanced models like GPT-4. This initiative calls for tools and resources for small companies to train ethical LLMs, promoting the development of more equitable and responsible AI systems.

MIT LicenseMIT

Synthetic Data for LLM De-Biasing

Overview

Large Language Models (LLMs) have become increasingly prevalent due to their utility in various applications. However, these models often inherit biases present in the training data, leading to ethical concerns. This project focuses on creating a feasible way for small companies to train LLMs on a budget while maintaining ethical standards by reducing bias.

Problem Description

LLMs, trained on vast datasets, tend to learn and replicate human biases embedded in the data. This replication can perpetuate and even amplify these biases, leading to unethical outcomes in their application. Traditional methods for de-biasing LLMs involve extensive human review and reinforcement learning from human feedback (RLHF), which are resource-intensive and often infeasible for smaller companies.

Group Position

In light of the significant ethical implications and the high costs associated with bias mitigation, our group proposes a solution that democratizes access to low-bias training data. By generating high-quality synthetic data using advanced LLMs like GPT-4, we aim to provide small companies with the resources needed to train more ethical models without prohibitive costs.

Proposed Solution

Our solution comprises three main components:

  1. Tool Codebase: A Command Line Interface (CLI) tool that interacts with APIs of high-end LLMs to generate synthetic data. The tool allows users to specify prompts and generate data that can be contributed to a public dataset.

  2. Public Dataset: An open-source synthetic dataset created from high-end "safe output" LLMs. This dataset will be curated to ensure low bias and high quality, facilitating the training of more ethical models.

  3. Service to Host Dataset: A cloud-based service to host and manage the synthetic dataset, enabling users to easily access and contribute to it.

Impact Analysis

Local Impact

Deploying this project locally, especially in tech hubs like the Tampa Bay Area, can stimulate job creation in data science and AI fields. Collaboration with academic institutions like the University of South Florida can foster research and development, enhancing the region's tech ecosystem.

Global, Economic, and Societal Impact

Globally, reducing biases in LLMs aligns with the UN's Sustainable Development Goals, particularly those focused on reducing inequalities and promoting justice. Economically, this project can drive innovation by making ethical AI development accessible to more players in the market. Societally, fairer AI systems can lead to more equitable outcomes in various applications, from hiring practices to access to services.

Environmental Impact

While the environmental footprint of generating synthetic data is a concern due to energy consumption, the project can explore sustainable practices to mitigate this impact. Using energy-efficient technologies and renewable energy sources can help address these concerns.

Timeline and Resources

Budget

  • Token Costs: Generating synthetic data incurs costs. For instance, using Google's Gemini at $21 per million tokens, we can produce around 3.57 billion tokens with $75,000, translating to approximately 21.4GB of synthetic data.
  • Operational Costs: Funding will also cover salaries for initial development and moderation, as well as hosting costs for the dataset.

Timeline

  • Month 1: Develop and test the CLI tool, generate the first 10GB of synthetic data, and benchmark its bias.
  • Months 2-6: Generate the remaining data, release the tool and dataset, and support the project through community contributions and moderation.

Conclusion

This project aims to set a new standard in ethical AI development by providing accessible tools and resources for bias mitigation in LLMs. By leveraging synthetic data, we can help democratize the ability to create fairer AI systems, fostering a more inclusive and equitable digital future.

Ethical Considerations

For detailed information on the ethical considerations related to this project, please see the Ethical Considerations document.

License

This project is licensed under the MIT License.

References