Refine large language models to boost AI performance

Open AI's ChatGPT is very popular, yet not ready for enterprise adoption due to accuracy, security & privacy concerns. By refining large language models this can be tackled. For organizations that operate in a regulated space, federated learning can be used to train on distributed data to unlock value while maintaining privacy.
Marie Roehm
Published 23 January 2023

In only 5 days OpenAI’s ChatGPT hit 1 million users.

This artificial intelligence (AI) -powered chatbot is assumed to revolutionize the way people use search engines and approach a wide range of expressive tasks. Despite its popularity, it is not yet ready for enterprise adoption due to concerns over accuracy, security, and privacy.

This blog will explore the importance of refining large language models, like ChatGPT, and why it is essential for businesses, especially for those that operate in regulated industries, such as healthcare or government. We will look at how to go about fine-tuning a model like ChatGPT, as well as the benefits that can be achieved from the process, such as increased accuracy and privacy.

What is ChatGPT able to accomplish?

Utilizing natural language processing (NLP) technology, ChatGPT can react to any text input a user submits by, for example, flexibly generating automated responses to customer service inquiries or summarizing news articles. It is accurate, more efficient, and cost-effective compared to traditional methods. By being able to process complex conversations and responding accordingly, it reduces time and costs and lowers the barrier for businesses and individuals for any work that involves text.

Why ChatGPT can't find enterprise adoption on its own, especially in regulated industries

Despite the impressive capabilities of ChatGPT, it is not yet ready for enterprise adoption on its own, especially when companies operate in highly regulated industries. This is primarily due to concerns over accuracy, security, and privacy.

Inaccuracies or mistakes made by AI-powered chatbots potentially lead to:

  • Organizations being exposed to legal repercussions

  • Negative consequences for patients or customers

Legal implications in regulated industries:

  • Obligations concerning the processing of confidential information and data

Security-related risks:

  • Risk of data misuse or data breaches

Until these issues are properly addressed, enterprises and companies operating in regulated industries should exercise caution when adopting AI-powered chatbots for their use cases. One possibility to adapt to the challenges mentioned above is taking a large language model, like ChatGPT, or GPT3 (ChatGPT's foundational model), and fine-tuning it to a specific purpose.

Benefits of refining large language models

Refining large language models to a specific purpose can open-up adoption, including in regulated industries such as pharma, healthcare, or government. Such fine-tuned models can result in significantly increased accuracy for a specific task or industry, greater security, and better privacy protection.

Fine-tuning a large language model involves, for example, including custom rule sets, industry-specific knowledge, implementing additional security protocols (for example, encryption or access controls), or adding specific data for additional training.

Ultimately, refining a large language model ensures that any errors made by an AI-powered chatbot are minimized. This equips organizations with an AI-application optimized for their specific needs while providing them and their customers peace of mind over accuracy, security, and privacy.

A real-world example: fine-tuning ChatGPT

For an example of how this principle has been successfully applied, let's consider Hyro, a company in the conversational AI space. Hyro has implemented ChatGPT into their product, creating a seamless process to assist their customers for all sorts of requests and tasks.

By prioritizing security, precision, and predictability, Hyro fine-tuned ChatGPT to avoid liability and risk and give their customers accurate results. They are taking the large language model and building on top of it to make it good for a specific purpose, ensuring that it meets the specific needs and requirements of their clients.

This is just one example of how companies in the business world can take advantage of the power of large language models by fine-tuning them for specific tasks and purposes. Other companies, such as, follow a similar approach. We believe that this trend will continue to grow and evolve as the market for language models continues to mature.

The potential of federated learning to fine-tune large language models

To fine-tune a large language model, you need access to the right data. Often, data is distributed across companies or geographies, making it hard to get access to. In case of sensitive data, regulatory constraints forbid access. To unlock value of distributed data, even when it's sensitive, platforms for federated learning, such as Apheris, are invaluable. Combined with the right privacy technologies, federated learning can preserve data privacy and IP.

The importance of refining large language models

In conclusion, the importance of refining large language models such as ChatGPT cannot be overstated. With careful attention to detail and consideration given to an organization’s specific needs, businesses can ensure that their AI-powered chatbot is optimized for accuracy, security, and privacy. When fine-tuning large language models federated learning can be used as a powerful tool to train on distributed data, preserving data privacy. Ultimately, by refining large language models, organizations can leverage AI technology safely and effectively in order to gain competitive advantage within their respective industries.

Data & analytics
Machine learning & AI
Federated learning & analytics
Collaborative data ecosystems
Share blog post to Linked InTwitter

Insights delivered to your inbox monthly

Related Posts