Dynamic Generative Large Language Model (LLM) for Continuous Situational Awareness

Topic Number: A244-064 OBJECTIVE The proposed SBIR topic aims to advance the capabilities of large language models (LLMs) by addressing critical challenges and enhancing functionalities relevant to military applications, particularly within the U.S. Army. DESCRIPTION This topic will accept Direct to Phase II submission only. Direct to Phase II proposals are accepted for a cost up to $2,000,000 for an 18-month period of performance. This project focuses on developing methodologies to detect and mitigate bias in model outputs, ensuring the generation of fair and unbiased information. It also seeks innovative solutions to identify and correct hallucinations (false information generation) to bolster the reliability of LLMs. Furthermore, the integration of multimodal inputs and outputs will be explored to broaden the application scope of LLMs beyond text, facilitating their use in analyzing diverse data types such as images and videos. Enhancements in text summarization are also targeted to efficiently condense large volumes of information into actionable intelligence. The ability to rapidly train, fine-tuning, and/or augment with external data sources LLMs in specialized focus areas such as Acquisition, Intelligence, Operations, and Logistics, enabling tailored applications that meet specific Army needs is desired. Lastly, this project should assist in identifying metrics for quantifying LLM performance to easily discern which trained models are best for a task.