An Overview of Large Language Models and a Novel, Large Language Model-Based Cognitive Architecture for Solving Open-Ended Problems

Abstract

Large Language Models (LLMs) offer new opportunities to devise automated implementation generation methods that can tackle problem solving beyond traditional methods, which usually require algorithmic specifications and use only static domain knowledge. LLMs can support devising new methods to support activities in tackling open-ended problems, like problem framing, exploring possible solving approaches, feature elaboration and combination, advanced implementation assessment, and handling unexpected situations. This paper presents a detailed overview of the current work on LLMs, including model prompting, retrieval-augmented generation (RAG), and reinforcement learning. It then proposes a novel, LLM-based Cognitive Architecture (CA) to generate programming code starting from verbal discussions in natural language, a particular kind of problem-solving activity. The CA uses four strategies, three top-down and one bottom-up, to elaborate, adaptively process, memorize, and learn. Experiments are devised to study the CA performance, e.g., convergence rate, semantic fidelity, and code correctness.