# Making New Connections: LLMs as Puzzle Generators for The New York Times' Connections Word Game

arXiv: https://arxiv.org/abs/2407.11240 · HTML: https://mlsanity.com/paper/2407.11240/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 3 of 3)

- [Large Language Model Guided Tree-of-Thought](https://mlsanity.com/paper/2305.08291/) (2023)
- [Benchmarking Cognitive Biases in Large Language Models as Evaluators](https://mlsanity.com/paper/2309.17012/) (2023)
- [Large Language Models and Games: A Survey and Roadmap](https://mlsanity.com/paper/2402.18659/) (2024)

## Built on by (most cited first, 4 of 4)

- [GPT for Games: An Updated Scoping Review (2020-2024)](https://mlsanity.com/paper/2411.00308/) (2024)
- [Exploring Large Language Models for Word Games:Who is the Spy?](https://mlsanity.com/paper/2503.15235/) (2025)
- [LLMs as Method Actors: A Model for Prompt Engineering and Architecture](https://mlsanity.com/paper/2411.05778/) (2024)
- [Video Game Level Design as a Multi-Agent Reinforcement Learning Problem](https://mlsanity.com/paper/2510.04862/) (2025)

## Abstract (arXiv)

The Connections puzzle is a word association game published daily by The New York Times (NYT). In this game, players are asked to find groups of four words that are connected by a common theme. While solving a given Connections puzzle requires both semantic knowledge and abstract reasoning, generating novel puzzles additionally requires a form of metacognition: generators must be able to accurately model the downstream reasoning of potential solvers. In this paper, we investigate the ability of the GPT family of Large Language Models (LLMs) to generate challenging and creative word games for human players. We start with an analysis of the word game Connections and the unique challenges it poses as a Procedural Content Generation (PCG) domain. We then propose a method for generating Connections puzzles using LLMs by adapting a Tree of Thoughts (ToT) prompting approach. We evaluate this method by conducting a user study, asking human players to compare AI-generated puzzles against published Connections puzzles. Our findings show that LLMs are capable puzzle creators, and can generate diverse sets of enjoyable, challenging, and creative Connections puzzles as judged by human users.