ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational
Applications
- URL: http://arxiv.org/abs/2312.01339v4
- Date: Fri, 26 Jan 2024 18:11:43 GMT
- Title: ArabIcros: AI-Powered Arabic Crossword Puzzle Generation for Educational
Applications
- Authors: Kamyar Zeinalipour, Mohamed Zaky Saad, Marco Maggini, Marco Gori
- Abstract summary: 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.
- Score: 11.881406917880287
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. Based on a dataset comprising over
50,000 clue-answer pairs, the generator employs fine-tuning, few/zero-shot
learning strategies, and rigorous quality-checking protocols to enforce the
generation of high-quality clue-answer pairs. Importantly, educational
crosswords contribute to enhancing memory, expanding vocabulary, and promoting
problem-solving skills, thereby augmenting the learning experience through a
fun and engaging approach, reshaping the landscape of traditional learning
methods. The overall system can be exploited as a powerful educational tool
that amalgamates AI and innovative learning techniques, heralding a
transformative era for Arabic crossword puzzles and the intersection of
technology and education.
Related papers
- From Arabic Text to Puzzles: LLM-Driven Development of Arabic Educational Crosswords [10.876144855651608]
This project addresses the scarcity of advanced educational tools tailored for the Arabic language.
By providing a culturally and linguistically relevant tool, our objective is to make learning more engaging and effective.
This tool not only advances educational paradigms but also sets a new standard in interactive and cognitive learning technologies.
arXiv Detail & Related papers (2025-01-19T12:57:34Z) - Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion [55.27025066199226]
This paper addresses the need for democratizing large language models (LLM) in the Arab world.
One practical objective for an Arabic LLM is to utilize an Arabic-specific vocabulary for the tokenizer that could speed up decoding.
Inspired by the vocabulary learning during Second Language (Arabic) Acquisition for humans, the released AraLLaMA employs progressive vocabulary expansion.
arXiv Detail & Related papers (2024-12-16T19:29:06Z) - Harnessing LLMs for Educational Content-Driven Italian Crossword Generation [10.137657521054356]
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.
arXiv Detail & Related papers (2024-11-25T21:13:25Z) - 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) - Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models [54.58108387797138]
We investigate the effectiveness of prompt learning in code intelligence tasks.
Existing automatic prompt design methods are very limited to code intelligence tasks.
We propose Genetic Auto Prompt (GenAP) which utilizes an elaborate genetic algorithm to automatically design prompts.
arXiv Detail & Related papers (2024-03-20T13:37:00Z) - 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) - Informative Text Generation from Knowledge Triples [56.939571343797304]
We propose a novel memory augmented generator that employs a memory network to memorize the useful knowledge learned during the training.
We derive a dataset from WebNLG for our new setting and conduct extensive experiments to investigate the effectiveness of our model.
arXiv Detail & Related papers (2022-09-26T14:35:57Z) - Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP [28.479149974110463]
Cryptic crosswords, the dominant crossword variety in the UK, are a promising target for advancing NLP systems.
We present a dataset of cryptic clues as a challenging new benchmark for NLP systems.
We also introduce a challenging data split, examine the meta-linguistic capabilities of subword-tokenized models, and investigate model systematicity by perturbing the wordplay part of clues.
arXiv Detail & Related papers (2021-04-17T18:54:00Z) - Language Generation with Multi-Hop Reasoning on Commonsense Knowledge
Graph [124.45799297285083]
We argue that exploiting both the structural and semantic information of the knowledge graph facilitates commonsense-aware text generation.
We propose Generation with Multi-Hop Reasoning Flow (GRF) that enables pre-trained models with dynamic multi-hop reasoning on multi-relational paths extracted from the external commonsense knowledge graph.
arXiv Detail & Related papers (2020-09-24T13:55:32Z) - Exploiting Structured Knowledge in Text via Graph-Guided Representation
Learning [73.0598186896953]
We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs.
Building upon entity-level masked language models, our first contribution is an entity masking scheme.
In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
arXiv Detail & Related papers (2020-04-29T14:22:42Z) - Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer [64.22926988297685]
Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP)
In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
arXiv Detail & Related papers (2019-10-23T17:37:36Z)
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.