Large Language Models, or LLMs for short, have revolutionized various industries with their remarkable ability to answer questions, generate essays, and even compose lyrics. These powerful tools, such as OpenAI’s ChatGPT and Google’s Bard, have tremendous implications for sectors like financial services, retail, supply chains, and healthcare. However, despite their potential, many organizations have yet to fully harness the benefits of LLMs. One significant barrier is the daunting task of building a proprietary model, involving extensive computing power, vast amounts of data, and in-depth knowledge. On the other hand, relying solely on LLMs accessible behind API paywalls raises concerns about data privacy.
In this keynote from ODSC East 2023, Hagay Lupesko, VP of Engineering at MosaicML, reveals why owning your own LLM is not only critical but also achievable for most organizations. By taking the plunge into owning their own LLMs, businesses can experience increased security, flexibility, and accuracy while protecting their data and intellectual property.
Lupesko highlights the key benefits and shares insights into the process. From training and deploying your own LLM, and how it offers numerous advantages over relying on third-party models to how contrary to popular belief, developing and owning an LLM is a mountain too high for most companies.
But let’s take a look at several key benefits that organizations can expect once they understand that owning their own LLM is not an insurmountable challenge.
Customization: For companies, this is a big one, and though it’s similar to flexibility, they’re not the same. When a company has control over its own LLM, there is the freedom to customize and fine-tune the model to cater specifically to match its business needs. In this case, they can train the model on its own proprietary data, industry-specific jargon, or internal knowledge. All of this allows the models to generate more relevant and domain-specific responses that are more likely to match their industry’s unique needs.
Enhanced Security: By owning your LLM, you regain control over your data because there isn’t a middleman. The thing is, when using external LLMs, sensitive information may be transmitted and stored outside your organization. This poses potential risks, and depending on the industry, high-risk compliance issues that could make any risk management team sweat. But with an in-house LLM, you can ensure data privacy, better manage compliance requirements and implement robust security measures tailored to your specific requirement and needs.
Flexibility: Proprietary LLMs provide the flexibility to customize and fine-tune models according to your organization’s unique needs. As you can imagine, not every company can fully benefit from a generalized model. That’s because external LLMs often have predefined limitations, preventing organizations from fully optimizing them for specific use cases and market conditions. So by owning your LLM, you have the freedom to adapt and modify the model as your business evolves, ensuring it targets current needs while wasting minimal resources.
Improved Accuracy: Generic LLMs are trained on vast amounts of diverse data, making them versatile but potentially less accurate for industry-specific tasks. Building your LLM allows you to train it on domain-specific data, leading to more precise results. Fine-tuning the model with your own data enables it to understand the nuances and intricacies of your industry, ultimately enhancing the accuracy of the generated outputs.
Cost Efficiency: Just like with any piece of hardware, in the short term there is an initial investment. But in the long run, when companies own their own LLM, they can see cost-effectiveness rise, especially if your organization requires substantial language processing capabilities. Instead of paying for API calls or licensing fees, having an in-house LLM allows businesses to leverage their capabilities without incurring ongoing expenses.
Offline Access: When you own an LLM, you can use it even in scenarios where an internet connection is not available, reliable, or isolated due to compliance requirements. This can be particularly useful in remote locations prone to infrastructure issues, during situations where network access is limited, or when the data they want to use to train their model must be siloed off networks connected to the internet. Offline access allows businesses to continue operating LLMs without interruptions.
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