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Automated Crossword Solving

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arxiv 2205.09665 v2 pith:PKEJYKWC submitted 2022-05-19 cs.CL

classification cs.CL
keywords crosswordsystempuzzlesolvingaccuracyansweringexistingpuzzles
verification ladder T0 review T1 audit T2 compute T3 formal
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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 2 Pith papers

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  1. Tree of Thoughts: Deliberate Problem Solving with Large Language Models

    cs.CL 2023-05 accept novelty 8.0 of 10

    Tree of Thoughts enables language models to solve complex planning tasks by generating, evaluating, and searching over coherent intermediate thoughts in a tree, raising Game of 24 success from 4% to 74% with GPT-4.

  2. Learning in Infinitesimal Non-Compositional Sketches

    cs.LG 2026-07 conditional novelty 5.0 of 10

    The paper defines infinitesimal non-compositionality as the tangent-lift of factorization failures in learning sketches, and proposes learning as converging to a final coalgebra of iterated tangent lifts.

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