Large language models are the next evolution of NLP models. They are large, both in terms of the number of parameters that they use for computation, and the amount of data used to train them. LLMs can perform new writing and reading tasks previously thought to be infeasible. This is exciting because it has huge potential to help analysts and operators save significant time and effort by enhancing their intelligence and operations workflows.

NLP is a discipline focused on training machines to perform language tasks like reading, writing, summarizing, translating language, or understanding sentiment. LLMs are specific types of neural network models that are used in NLP and trained on massive amounts of text data. LLMs learn to understand language on their own by identifying patterns in text data. LLM use cases and capabilities overlap with existing NLP models. Take a look at our Primer Engines model library to get a sense of the various capabilities they cover.

LLMs are more versatile than standard language models at generating fluent, human-like language, having a better understanding of the semantics and nuances of human language. In addition, the models can be further trained on small unlabeled datasets and quickly tailored to specific customer needs. LLMs are sometimes called few-shot-learners, which means that they can learn from only a handful of examples provided in natural language. That level of adaptation without task-specific training is a defining characteristic of LLMs and one of their most impactful attributes.

Standard language models are NLP models that are relatively smaller in size with fewer parameters and computational requirements. These models are pre-trained like LLMs but generally must be trained on specific tasks to perform at high accuracy. For example, a small language model trained to classify spam emails may not be able to generate good summaries for the email. This is because the tasks of spam email classification and email summarization require different types of language understanding that cannot be combined within the size of these smaller models.  To learn more, check out this blog.

LLM capabilities include summarization of finished intelligence, querying your knowledge base or documents in natural language, and reading and writing across multiple languages. These capabilities are extremely helpful for speeding up intelligence cycles and providing comprehensive situational awareness and early warning to protect troops and assets. Now every analyst and operator can interact fluently with data in natural language, and not just developers.

LLMs bring powerful new capabilities for National Security agencies, providing a potential critical advantage in certain situations. However, agencies need to balance several considerations in the context of their intended use case, including the model’s factual correctness, cost, and explainability. In certain cases, smaller models trained for specific tasks can meet your mission needs with high accuracy and in a cost-efficient manner. Primer can help you balance these considerations to determine what model is right for you.

No. ChatGPT is a generic application of GPT-3 LLMs from OpenAI. The application is great at “the blank page problem” and delivering tailored answers to your general questions, however it is not trained on your industry specific data corpus to serve specific mission requirements and needs.

It is essential to have the right safeguards, monitoring tools, and corrective processes to reach and sustain high levels of performance. While the accuracy of LLMs will continue to get better, known and unknown limitations of the technology can generate inaccurate outputs.

Computational costs of running AI applications can be high, especially LLMs on enterprise applications. The costs may vary depending on the use case, the type of model used and the size of the dataset being processed. In addition to infrastructure costs, it is important to consider if you have the right talent to help deploy these. If not, we strongly recommend you find the right partner that has proven NLP expertise in National Security. Primer is ready to help you think through these things and figure out what is right for you.

To successfully deploy LLMs for technological advantage, start by identifying what use cases and data you want to use them for. Consider the following: 1) Ensure that the LLMs meet strict security requirements and have necessary protocols to protect sensitive information; 2) Determine how the LLMs will integrate with existing systems, and identify potential technical challenges; 3) Estimate infrastructure cost to run LLMs on your data; 4) Develop a comprehensive training program for your team and troops to use LLMs effectively; 5) Establish a support structure for any technical issues; and 6) Define metrics to measure the effectiveness of LLMs, such as increased efficiency, improved accuracy, and reduced costs.