Eliciting problem specifications for LLM-Modulo cognitive systems

Abstract

Large language models (LLMs) offer unprecedented natural-language understanding and generation capabilities. However, evaluations of their ability to demonstrate other cognitive functions, especially various categories of reasoning, have been, at best, mixed. The limited scope of reliable and robust LLM capabilities has resulted in a new class of AI systems, LLM-Modulo AI, in which LLMs are used to contribute to the overall capabilities of an intelligent system. In this paper, we explore the applicability of LLMs for one specific capability acutely missing in most cognitive systems: problem formulation. Cognitive systems generally require a human to translate a problem definition into some specification that the cognitive system can use to attempt to solve the problem or perform the task. We explore how large language models (LLMs) can be utilized to map a problem class, defined in natural language, into a semi-formal specification that can then be utilized by an existing reasoning and learning system to solve instances from the problem class. The result is a Modulo-LLM cognitive system in which the LLM roughly acts as a cognitive task analyst, generating a problem specification that can be used by a typical cognitive system to solve specific problems. The agent uses prompts derived from the definition of problem spaces in the AI literature and general problem-solving strategies (Polya’s How to Solve It). We offer preliminary evidence illustrating the potential for LLM-based problem specification. Such automatic problem specification offers the potential to speed cognitive systems research via disintermediation of problem formulation while also retaining core capabilities of cognitive systems, such as robust inference and online learning.