Harnessing LLMs for Educational Content-Driven Italian Crossword Generation
- URL: http://arxiv.org/abs/2411.16936v1
- Date: Mon, 25 Nov 2024 21:13:25 GMT
- Title: Harnessing LLMs for Educational Content-Driven Italian Crossword Generation
- Authors: Kamyar Zeinalipour, Achille Fusco, Asya Zanollo, Marco Maggini, Marco Gori,
- Abstract summary: We unveil a novel tool for generating Italian crossword puzzles from text.
We use advanced language models such as GPT-4o, Mistral-7B-Instruct-v0.3, and Llama3-8b-Instruct.
This cutting-edge generator makes use of the comprehensive Italian-Clue-Instruct dataset.
- Score: 10.137657521054356
- License:
- 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.
Related papers
- Towards Visual Text Design Transfer Across Languages [49.78504488452978]
We introduce a novel task of Multimodal Style Translation (MuST-Bench)
MuST-Bench is a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems.
In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.
arXiv Detail & Related papers (2024-10-24T15:15:01Z) - TRINS: Towards Multimodal Language Models that Can Read [61.17806538631744]
TRINS is a Text-Rich image INStruction dataset.
It contains 39,153 text-rich images, captions, and 102,437 questions.
We introduce a Language-vision Reading Assistant (LaRA) which is good at understanding textual content within images.
arXiv Detail & Related papers (2024-06-10T18:52:37Z) - A Turkish Educational Crossword Puzzle Generator [10.434753479074814]
This paper introduces the first Turkish crossword puzzle generator designed to leverage the capabilities of large language models (LLMs)
It's a notable step in AI-enhanced education, merging game-like engagement with learning for Turkish and setting new standards for interactive, intelligent learning tools in Turkish.
arXiv Detail & Related papers (2024-05-11T15:18:56Z) - Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles [10.375451846093327]
We propose a methodology to build educational clue generation datasets that can be used to instruct Large Language Models.
By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues.
We used clue-instruct to instruct different LLMs to generate educational clues from a given input content and keyword.
arXiv Detail & Related papers (2024-04-09T10:12:34Z) - ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational
Applications [11.881406917880287]
This paper presents the first Arabic crossword puzzle generator driven by advanced AI technology.
Leveraging cutting-edge large language models including GPT4, GPT3-Davinci, GPT3-Curie, GPT3-Babbage, GPT3-Ada, and BERT, the system generates distinctive and challenging clues.
arXiv Detail & Related papers (2023-12-03T10:03:50Z) - Italian Crossword Generator: Enhancing Education through Interactive
Word Puzzles [9.84767617576152]
We develop a comprehensive system for generating and verifying crossword clues.
A dataset of clue-answer pairs was compiled to fine-tune the models.
For generating crossword clues from a given text, Zero/Few-shot learning techniques were used.
arXiv Detail & Related papers (2023-11-27T11:17:29Z) - Retrieval-Augmented Code Generation for Universal Information Extraction [66.68673051922497]
Information Extraction aims to extract structural knowledge from natural language texts.
We propose a universal retrieval-augmented code generation framework based on Large Language Models (LLMs)
Code4UIE adopts Python classes to define task-specific schemas of various structural knowledge in a universal way.
arXiv Detail & Related papers (2023-11-06T09:03:21Z) - TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild [102.93338424976959]
We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved instruction-following capabilities.
Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model.
To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models.
arXiv Detail & Related papers (2023-09-14T15:34:01Z) - Storyfier: Exploring Vocabulary Learning Support with Text Generation
Models [52.58844741797822]
We develop Storyfier to provide a coherent context for any target words of learners' interests.
learners generally favor the generated stories for connecting target words and writing assistance for easing their learning workload.
In read-cloze-write learning sessions, participants using Storyfier perform worse in recalling and using target words than learning with a baseline tool without our AI features.
arXiv Detail & Related papers (2023-08-07T18:25:00Z) - VidLanKD: Improving Language Understanding via Video-Distilled Knowledge
Transfer [76.3906723777229]
We present VidLanKD, a video-language knowledge distillation method for improving language understanding.
We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset.
In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models.
arXiv Detail & Related papers (2021-07-06T15:41:32Z) - Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as
a Target for NLP [5.447716844779342]
Cryptic crosswords are the dominant English-language crossword variety in the United Kingdom.
We present a dataset of cryptic crossword clues that can be used as a benchmark and train a sequence-to-sequence model to solve them.
We show that performance can be substantially improved using a novel curriculum learning approach.
arXiv Detail & Related papers (2021-04-17T18:54:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.