Large Language Model (LLM) as a System of Multiple Expert Agents: An
  Approach to solve the Abstraction and Reasoning Corpus (ARC) Challenge
John Chong Min Tan Mehul Motani
We attempt to solve the Abstraction and Reasoning Corpus (ARC) Challenge
using Large Language Models (LLMs) as a system of multiple expert agents. Using
the flexibility of LLMs to be prompted to do various novel tasks using
zero-shot, few-shot, context-grounded prompting, we explore the feasibility of
using LLMs to solve the ARC Challenge. We firstly convert the input image into
multiple suitable text-based abstraction spaces. We then utilise the
associative power of LLMs to derive the input-output relationship and map this
to actions in the form of a working program, similar to Voyager / Ghost in the
MineCraft. In addition, we use iterative environmental feedback in order to
guide LLMs to solve the task. Our proposed approach achieves 50 solves out of
111 training set problems (45%) with just three abstraction spaces - grid,
object and pixel - and we believe that with more abstraction spaces and
learnable actions, we will be able to solve more.