Tech Explainer: Large Language Models (LLMs)

What It Is

Large Language Models (LLMs) are evolving artificial intelligence (AI) systems that can understand, generate, and manipulate human language in new and amazingly complicated ways. These systems are one of the most significant advances in AI technology and they will undoubtedly change how humans interact with machines and process information.

At their core, LLMs are neural networks trained on massive text datasets, ranging from books and articles to websites, code, and countless other things. Through this training, the LLMs develop complex statistical models of language that allow them to predict and generate text in response to human inputs. Some examples you are probably familiar with are OpenAI's ChatGPT, Anthropic's Claude, and Google's Gemini. Think of them as sophisticated pattern recognition systems that have learned the complex relationships between words, concepts, and contexts in human communication.

LLMs can adapt to different contexts and tasks without explicit programming, unlike the more traditional language processing systems that relied on rules and predefined responses and had pretty constrained limits to their capabilities. These LLMs can understand the nuances in language, recognize unsaid meanings, and even perform complex reasoning tasks. This flexibility comes from their architecture, which uses multiple processing layers to analyze text at various levels, from basic grammar to complex semantic relationships.

Modern LLMs have shown capabilities we would have thought impossible just a few years ago. They can write code, analyze complex documents, engage in sophisticated dialogue, and demonstrate basic reasoning abilities. In some cases, they can seem convincingly human. However, it's crucial to understand that these systems don't truly "understand" language as we do. They are highly sophisticated pattern-matching systems that can produce human-like outputs based on their training data but are far from sentient.

Why It's Important

The significance of LLMs goes far beyond their apparent uses in chatbots and other content generation. These systems represent a massive shift in processing and understanding information. They could offer capabilities that transform entire sectors of society, industry, and national security if employed extensively.

In information processing, LLMs serve as powerful force multipliers. They can analyze massive amounts of text data in seconds, identify patterns, and connect the dots between all the information. That same task might take human analysts weeks or months to complete. This capability has immediate applications for our national security in intelligence gathering, threat assessment, and strategic planning. For example, an LLM could analyze thousands of news reports, social media posts, and documents on the internet to identify emerging trends and potential security threats.

Perhaps one of the most useful abilities is their ability to understand multiple languages with extreme accuracy, eliminating one of the biggest challenges to global information analysis. LLMs can provide near-instant translation while preserving cultural context and nuance—a capability that will likely have enormous implications for the intelligence community.

Furthermore, LLMs are great at tasks that traditionally require high human expertise, such as document analysis, report creation, and pattern recognition. They can automatically categorize and summarize documents, extract the most essential information, and even identify subtle connections between seemingly unrelated pieces of information, as many good intelligence analysts are trained to do. That being said, we are likely far from LLMs replacing human intelligence analysts, as these systems are accompanied by their own challenges and shortcomings. Still, if the intelligence community puts their mind to it, and I'm confident they will, LLMs could prove invaluable to our national security. 

How It Impacts U.S. National Security

LLMs' impact on national security is likely to be immense. They present significant opportunities, but also some serious challenges that must be addressed if we want to fully utilize this technology safely and efficiently.

Opportunities

For the intelligence community, LLMs represent a massive step forward in their ability to process and analyze information. These systems can constantly track and analyze vast amounts of open-source intelligence. They can simultaneously process/translate multiple languages and provide translation and cultural context to decision-makers that could take longer to produce. If the intelligence community effectively utilizes these systems and enhances their productivity and capabilities, it could prove a massive boon for national security. 

LLMs' decision-support capabilities will likely be equally impactful. These systems can analyze complex scenarios, generate potential courses of action, and help evaluate possible outcomes. They can also provide rapid situation assessments and help decision-makers understand what's going on quickly and accurately. This would be extremely valuable in time-sensitive situations requiring quick and informed decisions.

On the operational level, LLMs significantly increase efficiency across multiple domains. They can automate many routine tasks and allow humans to focus on more complex problems, such as those that require judgment and expertise. For example, LLMs can automatically generate intelligence summaries, process routine communications, and maintain up-to-date situation reports while freeing up valuable human resources for more critical tasks. Human professionals can use LLMs to greatly increase their productivity and decrease the time it takes to produce results. 

By automating many of the routine tasks, LLMs would enable human professionals to focus more on the tasks that require human attention, such as the ones that require judgment and expertise. For example, LLMs could be used to automatically generate summaries, compile and organize large amounts of data, and maintain up-to-date situation reports. Freeing humans up from these tedious but less complex tasks would enable them to spend more time on more valuable tasks that require human attention. 

Challenges

However, LLMs are also accompanied by significant security challenges. Adversaries can use these capabilities to enhance their operations and potentially create more sophisticated disinformation campaigns or improve their counterintelligence efforts. One example is Russia's use of AI -powered bots to create pro-Russian propaganda and spread misinformation in the U.S. The ability of LLMs to generate text that seems human makes it difficult at times to distinguish real communications from artificial ones.  Many countries would likely seek to produce artificial information to throw off or mislead opposing countries' intelligence services. 

There are also some serious concerns about data security and AI model integrity. LLMs require massive amounts of training data to be operational. Generally, the most important thing is how good the data is that a model was trained on. If you were training a model to assist with financial accounting, training it on all of the Game of Thrones books and the lyrics to every Taylor Swift song would be pretty unhelpful. So, ensuring that the training data is valuable information that aligns with what you want your model to be used for is essential. Furthermore, if an adversary were to "poison" a model's dataset by adding false or misleading information, it could cause the LLM to produce faulty or incomplete outputs. Even the slightest bit of bad data could lead to enormous problems when using these models for national security purposes. 

From a strategic perspective, LLMs represent a new frontier in the technological competition between countries, similar to previous technological revolutions like nuclear technology or space capabilities. This raises important questions about model access, training data security, and the need for thorough governance frameworks.

The future implications of LLM technology for national security are still emerging. Still, it's abundantly clear that these systems will play a very important role in the future of our nation's security. Success will require leveraging these capabilities effectively and developing comprehensive strategies to protect against their potential misuse or malicious use by adversaries. 

Tech Explainers are our method of introducing and analyzing complex technologies in an easily digestible way. They are good practice for us as we grow in our own knowledge and become better at “translating” the technological side of things into the policy and national security side. 

These technologies are incredibly complicated and thus it is difficult to address every detail. So while we strive to produce the best explanations we can, we may have overlooked something in the process. If you feel that is the case please reach out and let us know. We’re always happy to talk!