# Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles

arXiv: https://arxiv.org/abs/2404.06186 · HTML: https://mlsanity.com/paper/2404.06186/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 9 Apr 2024.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs](https://mlsanity.com/paper/1401.4597/) (2014)
- [Italian Crossword Generator: Enhancing Education through Interactive Word Puzzles](https://mlsanity.com/paper/2311.15723/) (2023)
- [ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational Applications](https://mlsanity.com/paper/2312.01339/) (2023)

## Built on by (most cited first, 5 of 5)

- [Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER](https://mlsanity.com/paper/2407.01272/) (2024)
- [A Turkish Educational Crossword Puzzle Generator](https://mlsanity.com/paper/2405.07035/) (2024)
- [Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses](https://mlsanity.com/paper/2408.00584/) (2024)
- [Harnessing LLMs for Educational Content-Driven Italian Crossword Generation](https://mlsanity.com/paper/2411.16936/) (2024)
- [left|,circlearrowright,boxedBUS,right|: A Large and Diverse Multimodal Benchmark for evaluating the ability of Vision-Language Models to understand Rebus Puzzles](https://mlsanity.com/paper/2511.01340/) (2025)

## Abstract (arXiv)

Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for traditional crosswords, educational clue-answer pairs datasets are missing. In this article, we propose a methodology to build educational clue generation datasets that can be used to instruct Large Language Models (LLMs). By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues related to the given input keyword and its context. With such an approach, we created clue-instruct, a dataset containing 44,075 unique examples with text-keyword pairs associated with three distinct crossword clues. We used clue-instruct to instruct different LLMs to generate educational clues from a given input content and keyword. Both human and automatic evaluations confirmed the quality of the generated clues, thus validating the effectiveness of our approach.