1. Natural Language Processing in Multiple Languages
Intercepted communications, social media posts full of slang and high-volume data feeds represent a firehose of valuable information in a variety of languages and dialects. LLMs can provide nearly instantaneous translation of these types of communications, acting as a force multiplier for human translators.
Just as important, LLMs can take translated or native English information and perform natural language processing, such as content categorization and sentiment analysis — identifying disinformation, coordination of extremist actions or just general mood — which can help IC agencies get ahead of emerging threats and prioritize the most critical intelligence.
2. Contextual Analysis With Retrieval-Augmented Generation
Retrieval-augmented generation is an “add-on” technology that IC developers can use to securely feed classified data sources and the most current context and situational information to the LLM as an analyst interacts with the model. With RAG and a good LLM, analysts are not restricted to asking questions based on the LLM’s training, which can be months or years out of date. Instead, RAG-based applications prompt the LLM with additional context and data based on the analyst’s query. This lets the LLM work on the latest data, improves accuracy and reduces AI hallucinations without having to retrain or fine-tune the LLM.
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3. Better Document Search, Retrieval, Synthesis and Summarization
LLMs are good at generating concise summaries of lengthy data feeds, such as long reports or briefings from a single source; for example, writing an executive summary that the author forgot to include. Or, LLMs can take multiple information sources on a single topic and identify key themes that reflect the many points of view. When huge amounts of information need to be understood in a hurry, an LLM can condense and summarize quickly — without human bias — to help high-level decision-makers and analysts focus on their next steps.
