Harnessing LLMs for Educational Content-Driven Italian Crossword Generation
Authors: Kamyar Zeinalipour, Achille Fusco, Asya Zanollo, Marco Maggini, Marco Gori (Università di Siena, IUSS, 2024)
Topic: Language Models

Abstract
In this work, we unveil a novel tool for generating Italian crossword puzzles from text, utilizing advanced language models such as GPT-4o, Mistral-7B-Instruct-v0.3, and Llama3-8b-Instruct. Crafted specifically for educational applications, this cutting-edge generator makes use of the comprehensive Italian-Clue-Instruct dataset, which comprises over 30,000 entries including diverse text, solutions, and types of clues. This carefully assembled dataset is designed to facilitate the creation of contextually relevant clues in various styles associated with specific texts and keywords. The study delves into four distinctive styles of crossword clues: those without format constraints, those formed as definite determiner phrases, copular sentences, and bare noun phrases. Each style introduces unique linguistic structures to diversify clue presentation. Given the lack of sophisticated educational tools tailored to the Italian language, this project seeks to enhance learning experiences and cognitive development through an engaging, interactive platform. By meshing state-of-the-art AI with contemporary educational strategies, our tool can dynamically generate crossword puzzles from Italian educational materials, thereby providing an enjoyable and interactive learning environment. This technological advancement not only redefines educational paradigms but also sets a new benchmark for interactive and cognitive language learning solutions.

Introduction
While traditionally valued for their challenge and entertainment, crossword puzzles are increasingly recognized for their educational benefits. They provide an interactive learning environment that enhances the retention of both technical terms and general language skills, hence facilitating learning across various disciplines, improving language acquisition, and supporting cognitive development, through critical thinking and memory retention [1, 2, 3, 4, 5, 6, 7, 3, 8, 9, 2, 10, 11]. The integration of Natural Language Processing (NLP) and Large Language Models (LLMs) has further enhanced their effectiveness by providing sophisticated, contextually relevant clues for educational crosswords. This paper presents a novel tool that uses LLMs to generate tailored Italian educational crossword puzzles from texts, offering various clue types. By integrating user-provided texts or keywords and applying fine-tuning techniques, the tool produces high-quality clues and answers, offering educators a resource to develop more interactive and effective instructional methods. Furthermore, a new dataset called 1 has been compiled and will be released to the scientific community. The layout of this paper is organized in the following manner: Section 2 surveys the relevant literature in detail. Section 3 explains the methods used for dataset collection and curation. In Section 3, we describe the computational techniques employed in our study. Section 4 reports the results derived from our experimental analysis. Finally, Section 5 closes with conclusive insights and the broader implications of our research findings.

Conclusion
A novel system for generating crossword clues from Italian text is introduced, leveraging the newly developed Italian-Clue-Instruct dataset. This dataset, which includes text, keywords, categories, and related crossword clues in Italian, is pioneering in this field. By fine-tuning two large language models (LLMs), Mistral-7B-Instruct-v0.3 and Llama3-8b-Instruct, using this dataset, we have achieved significant improvements in the models’ ability to generate crossword clues from given text. The results highlight a substantial enhancement in model performance after fine-tuning. Both the Italian-Clue-Instruct dataset and the fine-tuned models are now publicly available, providing valuable tools for students and teachers to create educational crossword puzzles from Italian text. Future research will aim to develop models capable of generating various types of crossword clues, including fill-in-the-blank clues.