Fusion of Abstract Learning and Context-Optimized Neural-methods (FALCON)

Objective: The goal of this effort is to combine advanced machine learning (ML) methods that can be computationally efficient in structured data with large language models (LLM) that are general and can extract context from data. The aim is to derive powerful and efficient technology for interactive statistical analysis of large-scale data seen such as in enterprise or battlefield. Description: By integrating the contextualization power of LLMs with the statistical power of ML, the program aims to leverage the benefits of both to provide domain-specific statistical contextualization of structured data. This effort must: Survey and research the emerging ML methods suitable for large scale data containing both structured and unstructured data. Develop an architecture to combine select ML methods with LLM models. Determine a set of metrics that encompass accuracy, new insights, computational efficiency, and ability to generalize across datasets. Evaluate the combined architecture and methods in datasets drawn from multiple applications. This may focus on tabular data in enterprise or engineering applications. Develop and demonstrate methods to mitigate possible hallucination in the workflow and demonstrate verifiable and reproducible analytic traces. Demonstrate interactive analysis by incorporating new insights as they develop during the course of analysis. Keywords: Neuro-symbolic AI, large scale structured data analysis, decision theory, CMMC Level: Level 2 (Self)