At Apheris, we, like everyone else, are fascinated by the potential of ChatGPT. It has rightly taken the world by storm in recent months as millions of users have flocked to OpenAI’s site to experience firsthand the capabilities and limitations of the famous chatbot, to varying degrees of success.
We sought to test the chatbot out for ourselves, quizzing it (rather ironically) on the relationship between large language models and federated learning, and how the former benefits the latter.
Throughout this blog, we’ll showcase what ChatGPT had to say, and throw in our own thoughts for good measure. Here’s a snippet of how the conversation went.
We’re off to a great start. This is true, though it must be noted that in order to create more customized models, organizations must be able to access the data needed to fine-tune the foundational model. This can prove difficult as the data needed is typically distributed across departments, companies, and even different geographic regions. Unfortunately, regulatory constraints can prevent organizations from pooling this scattered data into one usable set.
But there is a solution. Used in conjunction with the right privacy technologies, organizations can access this data, no matter where it sits, through a federated learning platform. This allows them to drive better, more personalized models on data that doesn’t need to be moved, guaranteeing privacy and peace of mind to each data owner without stifling data collaboration. Plus, it offers the added benefit of removing the time and costs associated with centralizing data and creating complex data sharing agreements.
Yes and no. It’s true that federated learning favors decentralization and that large language models will need to generalize to a large data set, but that doesn’t mean that performance must suffer.
In fact, when it comes to fine-tuning large language models to diverse data sources, federated learning can be an invaluable tool. For example, federated learning allows for the training of large language models on distributed data. This, in turn, can have a positive impact on performance, as it allows for the customization of speed, tighter privacy controls and more accurate models. Likewise, by combining data from multiple sources whilst still maintaining control of owned data, owners will create a good first line of compliance that puts them a step ahead of regulation requirements.
Ultimately, these features help organizations leverage AI and ML technologies safely and efficiently, resulting in greater competitive insights and breakthroughs.
This is true, but it certainly isn’t as challenging as our chatbot friend suggests. Federated learning was never designed to be the sole solution to preserve data privacy.
That’s why, despite significant privacy-enhancing advantages when compared to centralized data sharing, federated learning should always be used in conjunction with other privacy-enhancing technologies and enterprise-grade security tools, secure architectures, and process-defining frameworks. Combined, these building blocks ensure that text-based and other sensitive data sources are not accidentally exposed.
We couldn’t agree more. In ChatGPT’s case, large language models combined with federated learning would enable it to be more accurate and even more efficient with its responses, therefore allowing it to help even more businesses save time and money when seeking assistance with text-based tasks.
While certainly impressive, ChatGPT has some ways to go before it can become the true, trusted source of information it’s destined to be. To realize its potential, OpenAI must forge a path towards the use of foundational models, such as the recently announced GPT-4, as a building block for others to build their own AI ambitions upon. In this instance, organizations will be empowered to use their own data, as well as any collaborative third-party data, while retaining control and building better, more accurate models, even in sensitive or highly regulated industries.