Making New Connections: LLMs as Puzzle Generators for The New York Times' Connections Word Game
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, generati...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2407.11240 |