Automated Crossword Solving
Eric Wallace, Nicholas Tomlin, Albert Xu, Kevin Yang, Eshaan Pathak, Matthew L. Ginsberg, Dan Klein
Abstract
We present the Berkeley Crossword Solver, a state-of-the-art approach for automatically solving crossword puzzles. Our system works by generating answer candidates for each crossword clue using neural question answering models and then combines loopy belief propagation with local search to find full puzzle solutions. Compared to existing approaches, our system improves exact puzzle accuracy from 71% to 82% on crosswords from The New York Times and obtains 99.9% letter accuracy on themeless puzzles. Additionally, in 2021, a hybrid of our system and the existing Dr.Fill system outperformed all human competitors for the first time at the American Crossword Puzzle Tournament. To facilitate research on question answering and crossword solving, we analyze our system's remaining errors and release a dataset of over six million question-answer pairs.
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Cited by top-tier papers4
- Tree of Thoughts: Deliberate Problem Solving with Large Language ModelsShunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran et al.NeurIPS 2023 · 5,068 citations
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- Generalizable Reasoning through Compositional Energy MinimizationAlexandru Oarga, Yilun DuNeurIPS 2025 · 3 citations
- A Reasoning-Based Approach to Cryptic Crossword Clue SolvingMartin Andrews, Sam WitteveenICML 2025
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- AmbigQA: Answering Ambiguous Open-domain QuestionsSewon Min, Julian Michael, Hannaneh Hajishirzi, Luke ZettlemoyerEMNLP 2020 · 162 citations
- Dense Passage Retrieval for Open-Domain Question AnsweringVladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis et al.EMNLP 2020 · 142 citations
- Offline Model-Based Optimization via Normalized Maximum Likelihood EstimationJustin Fu, Sergey LevineICLR 2021 · 59 citations
- Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLPJosh Rozner, Christopher Potts, Kyle MahowaldNeurIPS 2021 · 27 citations
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