  Crosswords are a form of word puzzle that require a solver to demonstrate a
high degree of proficiency in natural language understanding, wordplay,
reasoning, and world knowledge, along with adherence to character and length
constraints. In this paper we tackle the challenge of solving crosswords with
Large Language Models (LLMs). We demonstrate that the current generation of
state-of-the art (SoTA) language models show significant competence at
deciphering cryptic crossword clues, and outperform previously reported SoTA
results by a factor of 2-3 in relevant benchmarks. We also develop a search
algorithm that builds off this performance to tackle the problem of solving
full crossword grids with LLMs for the very first time, achieving an accuracy
of 93\% on New York Times crossword puzzles. Contrary to previous work in this
area which concluded that LLMs lag human expert performance significantly, our
research suggests this gap is a lot narrower.
