Joshua M. Ackerman

George Cybenko, Joshua Ackerman, Paul Lintilhac

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.

Soyeon Choi, Kangwook Lee, Oliver Sng et al.

How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to third places (e.g., cafe, store, park), and fewer conversations throughout the town. In interview responses, agents explicitly attributed their behavioral changes to disease-avoidance motivations. A validity check further indicated that they could distinguish between infectious and noninfectious diseases, selectively reducing social engagement only when there was a risk of infection. Our findings highlight the potential of GABM as an experimental tool for exploring complex human social dynamics at scale.

Joshua Ackerman, George Cybenko

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.