AI-Assisted Wordle Demo: Combining LLMs and Rule-Based Solvers for Enhanced Gameplay

This demo paper<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>1Demo Paper investigates the use of Large Language Models (LLMs) to enhance Wordle and its variant, Fibble. The program allows players to request hints from either a built-in rule-based solver or an LLM, ensuring consistent guesses based on previous feedback. A retry mechanism is employed to refine guesses, with up to five attempts per request. Experimental results show that advanced models like o1-preview achieve a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${9 5. 7 4 \%}$</tex> win rate in Wordle, though performance drops in Fibble due to deceptive feedback. This work demonstrates the potential of LLMs for interactive problem-solving in gaming contexts.