Large language models (LLMs) have become the backbone of the Generative AI revolution. They are complex algorithms that use machine learning techniques to summarize, predict and even generate human-like content using very large datasets. They are trained on massive amounts of text data that help them understand the intricacies of language. And they are completely transforming everything from research and marketing to customer service and business operations.
However, despite their power and ease of use, privacy and security risks are becoming major concerns for companies that rely on public LLMs such as ChatGPT. To address these concerns, private LLMs are emerging as a viable alternative for enterprises that take privacy and data protection seriously. This is especially true within the life sciences industry, which has strict privacy regulations. These companies need to ensure that their data is always protected without sacrificing the value that LLMs bring.
According to P360 CEO Anupam Nandwana, private LLMs are becoming increasingly popular in this industry due to their ability to protect and keep bad data from polluting results.
“Private LLMs provide life sciences companies with a walled garden that protects their data and ensures that outputs aren’t being influenced by bad data,” said Nandwana. “Generative AI trained from a private LLM operates within a controlled environment, where the dataset is meticulously curated to align with specific guidelines, quality standards and desired outcomes. This same walled garden also prevents proprietary company data from going out into the public domain.”
New Jersey-based P360 is a leading technology developer for the life sciences industry. The company’s products range from healthcare professional engagement tools to an AI-driven marketing and sales enablement platform. Nandwana says that the company works with some of the biggest names in the industry.
Private LLM Have Fewer Hallucinations
The key difference between private and public LLMs is that private LLMs only use data from within the company, while public LLMs use a combination of public and private data. And it’s this use of public data that causes those AIs to hallucinate.
AI hallucination occurs when AI-composed content contains information that's unsupported by facts. Although public LLMs attend to a vast sea of data points, the sheer volume of data they mine makes it difficult to remove information that is erroneously gleaned. In identifying false information, the AI system is exposed to invisible edges between different categories in the training dataset. Since it has not been trained to differentiate these categories, it sometimes fills in gaps with made-up information.
“Safety is a significant concern in the life sciences industry,” said Nandwana. “AI hallucination can lead to significant safety concerns, especially regarding medical devices and medicines. For instance, if an AI hallucinates when generating data about a medication, it can potentially wind up harming a patient.”
How Should Enterprises Go About Setting Up a Private LLM?
Deploying a Private LLM has the potential to revolutionize any life sciences organization. By harnessing its power, organizations can gain a distinct advantage by accelerating content development, automating customer interactions and extracting valuable insights from vast amounts of unstructured data. However, achieving a successful deployment can sometimes be overwhelming.
From selecting the perfect platform to integrating relevant data sets, organizations face critical decisions that can make or break their LLM implementation. And one of the biggest questions is whether a company should build their own private LLM or use an API.
“Building a custom LLM offers the advantage of tailoring it to an organization’s unique business objectives,” said Nandwana. “However, this option can be expensive and time-consuming. It requires a team of LLM experts, which can be like searching for a needle in a haystack amidst the growing AI landscape.”
Fortunately, there is another option. By utilizing an API, companies can tap into the power of an existing LLM without requiring extensive resources. However, it is an option that can be fraught with risk.
The Risk of Using APIs to Develop Private LLMs
In an effort to mitigate development costs, some companies are now turning to APIs to develop their private LLMs. The primary advantage of taking this route is cost reduction. By utilizing APIs, life sciences organizations can leverage pre-built models and algorithms without significantly investing in infrastructure development. This translates to less work, less effort and less expense.
However, there are notable drawbacks to using APIs. One disadvantage is that there is a higher risk of data breaches. When utilizing APIs, companies are effectively handing over data to a third party. And although the third-party developer might promise to keep data secure, sensitive information like intellectual property can still be compromised.
In a world where intellectual property is king, the potential loss of it is something that needs to be thoroughly considered. Additionally, APIs may not always be compatible with the data you provide. But if this option is what your organization has landed on, Nandana his this advice.
“To minimize risk, companies should only work with proven developers with experience in the life sciences industry,” he said. “There are a lot of advantages to using an API, but it’s important for companies to do their homework.”
Customized Solutions are the Answer
If APIs have risk, and developing your own LLM is out of reach for most companies, what’s the solution? According to Nandwana, it’s a customized solution.
“Customized solutions allow life sciences companies to address the specific challenges and opportunities they face,” he said. “This could include developing language models to identify adverse events in clinical trial data, improve sales, or analyze social media chatter to understand patient sentiment about certain treatments or illnesses. A customized solution provider can work directly with a life sciences company to identify these challenges and develop a bespoke model to address them.”
Leveraging a customized solution can also help ensure the quality and accuracy of the language model. These solutions can help life sciences companies address specific regulatory and compliance requirements. The life sciences industry is highly regulated, and companies must ensure that their language models meet these requirements.
Final Thoughts
Private LLMs offer life science companies a safe and effective way to work with data, providing accurate and reliable outcomes. However, it’s essential for life science companies to carefully vet the options available before deciding whether to build in-house, use APIs, or rely on public LLMs for their needs. Customized private LLMs offer a concrete way for companies to meet their needs while remaining compliant and protecting their intellectual property.
