TEL'M: Test and Evaluation of Language Models

Language Models have demonstrated remarkable capabilities on some tasks while failing dramatically on others. The situation has generated considerable interest in understanding and comparing the capabilities of various Language Models (LMs) but those efforts have been largely ad hoc with results that are often little more than anecdotal. This is in stark contrast with testing and evaluation processes used in healthcare, radar signal processing, and other defense areas. In this paper, we describe Test and Evaluation of Language Models (TEL'M) as a principled approach for assessing the value of current and future LMs focused on high-value commercial, government and national security applications. We believe that this methodology could be applied to other Artificial Intelligence (AI) technologies as part of the larger goal of "industrializing" AI.

A Survey of Neural Networks and Formal Languages

This report is a survey of the relationships between various state-of-the-art neural network architectures and formal languages as, for example, structured by the Chomsky Language Hierarchy. Of particular interest are the abilities of a neural architecture to represent, recognize and generate words from a specific language by learning from samples of the language.