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Abstract：
Large Language Models (LLMs) offer advanced text generation capabilities, sometimes surpassing human abilities. However, their use without proper expertise poses significant challenges, particularly in educational contexts. This article explores different facets of natural language generation (NLG) within the educational realm, assessing its advantages and disadvantages, particularly concerning LLMs. It addresses concerns regarding the opacity of LLMs and the potential bias in their generated content, advocating for transparent solutions. Therefore, it examines the feasibility of integrating OpenLogos expert-crafted resources into language generation tools used for paraphrasing and translation. In the context of the Multi3Generation COST Action (CA18231), we have been emphasizing the significance of incorporating OpenLogos into language generation processes, and the need for clear guidelines and ethical standards in generative models involving multilingual, multimodal, and multitasking capabilities. The Multi3Generation initiative strives to progress NLG research for societal welfare, including its educational applications. It promotes inclusive models inspired by the Logos Model, prioritizing transparency, human control, preservation of language principles and meaning, and acknowledgment of the expertise of resource creators. We envision a scenario where OpenLogos can contribute significantly to inclusive AI-supported education. Ethical considerations and limitations related to AI implementation in education are explored, highlighting the importance of maintaining a balanced approach consistent with traditional educational principles. Ultimately, the article advocates for educators to adopt innovative tools and methodologies to foster dynamic learning environments that facilitate linguistic development and growth.
Large Language Models boast advanced text generation quality and capabilities, often surpassing those of humans. However, they also pose significant challenges when used without proper expertise or care. In an educational context, the examination of language generation tools and their use by students is vital for establishing guidelines and a shared understanding of their ethical usage. This article explores several aspects of language generation within an educational context, and showcases the potential use of OpenLogos resources, provided within the framework of the Multi3Generation COST Action (CA18231) in language study and their integration into language learning tools, such as paraphrasing (monolingual) and translation (bilingual or multilingual). This article emphasizes the importance of leveraging OpenLogos in education, especially in language learning or language enhancement contexts. By embracing innovative tools and methodologies, educators can nurture a dynamic and enriching learning environment conducive to linguistic growth and development.
Large Language Models boast advanced text generation quality and capabilities, often surpassing those of humans. However, they also pose significant challenges when used without proper expertise or care. In an educational context, the examination of language generation tools and their use by students is vital for establishing guidelines and a shared understanding of their ethical usage. This article explores several aspects of language generation within an educational context, and showcases the potential use of OpenLogos resources, provided within the framework of the Multi3Generation COST Action (CA18231) in language study and their integration into language learning tools, such as paraphrasing (monolingual) and translation (bilingual or multilingual). This article emphasizes the importance of leveraging OpenLogos in education, especially in language learning or language enhancement contexts. By embracing innovative tools and methodologies, educators can nurture a dynamic and enriching learning environment conducive to linguistic growth and development.
摘要：
大型语言模型(LLM)提供高级文本生成功能，有时超越人类的能力。然而,在没有适当专业知识的情况下使用它们会带来重大挑战，特别是在教育环境中。本文探讨了教育领域内自然语言生成（NLG）的不同方面，评估其优缺点，特别是关于LLM。它解决了有关LLM的不透明度和其生成内容的潜在偏见的问题，倡导透明的解决方案。因此,它研究了将OpenLogos专家制作的资源集成到用于释义和翻译的语言生成工具中的可行性。在Multi3GenerationCOSTAction(CA18231)的背景下，我们一直在强调将OpenLogos纳入语言生成过程的重要性，以及在涉及多语言的生成模型中需要明确的指导方针和道德标准，多模态,和多任务处理能力。Multi3Generation计划致力于推进NLG对社会福利的研究，包括其教育应用。它提倡受Logos模型启发的包容性模型，优先考虑透明度，人类控制,语言原则和意义的保存，并承认资源创造者的专业知识。我们设想了一个场景，OpenLogos可以为包容性AI支持的教育做出重大贡献。探讨了与教育中人工智能实施相关的伦理考虑和局限性，强调保持与传统教育原则相一致的平衡方法的重要性。最终,本文主张教育工作者采用创新的工具和方法，以促进语言发展和成长的动态学习环境。
大型语言模型拥有先进的文本生成质量和功能，往往超过人类。然而,如果没有适当的专业知识或护理，它们也会带来重大挑战。在教育背景下，对语言生成工具及其被学生使用的检查对于建立准则和对其道德使用的共同理解至关重要。本文探讨了教育背景下语言生成的几个方面，并展示了OpenLogos资源的潜在用途，在语言研究中的Multi3GenerationCOSTAction（CA18231）的框架内提供，并将其集成到语言学习工具中，例如释义（单语）和翻译（双语或多语种）。本文强调了在教育中利用OpenLogos的重要性，尤其是在语言学习或语言增强环境中。通过拥抱创新的工具和方法，教育者可以培养有利于语言成长和发展的动态和丰富的学习环境。
大型语言模型拥有先进的文本生成质量和功能，往往超过人类。然而,如果没有适当的专业知识或护理，它们也会带来重大挑战。在教育背景下，对语言生成工具及其被学生使用的检查对于建立准则和对其道德使用的共同理解至关重要。本文探讨了教育背景下语言生成的几个方面，并展示了OpenLogos资源的潜在用途，在语言研究中的Multi3GenerationCOSTAction（CA18231）的框架内提供，并将其集成到语言学习工具中，例如释义（单语）和翻译（双语或多语种）。本文强调了在教育中利用OpenLogos的重要性，尤其是在语言学习或语言增强环境中。通过拥抱创新的工具和方法，教育者可以培养有利于语言成长和发展的动态和丰富的学习环境。
