Decision Making in LLMs: A First Step

Christopher B Rauch

Abstract: This paper describes a study aimed at determining whether large-language models (LLMs) demonstrate they know howproblems and solutions connect. This question is part of formulating the more general question of whether current LLMs are capable of constructing new solutions to previously unseen problems. Our motivation comes from a 2019 award-winning challenge that artificial intelligence (AI) algorithms should be capable of examining problems to creatively imagine and evaluate solutions to those problems. For studying this general question, we adopt a model of decision making that shows that imagining solutions to problems is part of decision making thus rephrasing our question to asking whether LLMs can execute decision-making. We conclude that although LLMs can generate contents in answer to potentially any question, their responses lack precision and can hence benefit from a case-based reasoning (CBR) module. On the other hand, CBR can benefit from LLMs’ natural language generation to learn cases and associations between problems and solutions.