Summary of Clue-instruct: Text-based Clue Generation For Educational Crossword Puzzles, by Andrea Zugarini et al.
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
by Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini, Marco Gori, Leonardo Rigutini
First submitted to arxiv on: 9 Apr 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 This paper proposes a methodology for building datasets that can be used to train Large Language Models (LLMs) to generate educational crossword clues. The authors gather informative content from Wikipedia pages associated with relevant keywords and use LLMs to automatically generate pedagogical clues related to the input keyword and context. The resulting dataset, clue-instruct, contains 44,075 unique examples of text-keyword pairs with three distinct crossword clues each. The authors evaluate the quality of the generated clues using both human and automatic assessments, confirming the effectiveness of their approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a way to train computers to make educational crossword puzzles. Right now, there are no big datasets for educational crosswords, so this research helps fill that gap. The method uses Wikipedia pages and special language models to generate clues related to specific keywords. This means that large language models can be trained to create educational crossword puzzles from given text and keywords. The authors tested their approach and found that it works well. |
