Summary of Language Models Are Crossword Solvers, by Soumadeep Saha and Sutanoya Chakraborty and Saptarshi Saha and Utpal Garain
Language Models are Crossword Solvers
by Soumadeep Saha, Sutanoya Chakraborty, Saptarshi Saha, Utpal Garain
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper tackles the challenge of solving crosswords using large language models (LLMs). It demonstrates that current LLMs show significant competence in deciphering cryptic crossword clues, outperforming previous state-of-the-art results by a factor of 2-3. The authors also develop a search algorithm that utilizes this performance to solve full crossword grids for the first time with out-of-the-box LLMs, achieving an accuracy of 93% on New York Times puzzles. Furthermore, they show that LLMs generalize well and can provide answers with sound rationale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to use big language models to solve crosswords. It shows that these models are really good at understanding tricky clues and can even fill in whole crossword grids correctly! They tested it on New York Times puzzles and got 93% right. The models are also smart enough to explain why they chose an answer. |
