Large Language Models for Education: A Survey

• URL: http://arxiv.org/abs/2405.13001v1
• Date: Sun, 12 May 2024 01:50:01 GMT
• Title: Large Language Models for Education: A Survey
• Authors: Hanyi Xu, Wensheng Gan, Zhenlian Qi, Jiayang Wu, Philip S. Yu,
• Abstract summary: Large language models (LLMs) have been increasingly used in various applications. The use of LLMs for smart education (LLMEdu) has been a significant strategic direction for countries worldwide. While LLMs have shown great promise in improving teaching quality, changing education models, and modifying teacher roles, the technologies are still facing several challenges.
• Score: 32.42330148200439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) has a profound impact on traditional education. In recent years, large language models (LLMs) have been increasingly used in various applications such as natural language processing, computer vision, speech recognition, and autonomous driving. LLMs have also been applied in many fields, including recommendation, finance, government, education, legal affairs, and finance. As powerful auxiliary tools, LLMs incorporate various technologies such as deep learning, pre-training, fine-tuning, and reinforcement learning. The use of LLMs for smart education (LLMEdu) has been a significant strategic direction for countries worldwide. While LLMs have shown great promise in improving teaching quality, changing education models, and modifying teacher roles, the technologies are still facing several challenges. In this paper, we conduct a systematic review of LLMEdu, focusing on current technologies, challenges, and future developments. We first summarize the current state of LLMEdu and then introduce the characteristics of LLMs and education, as well as the benefits of integrating LLMs into education. We also review the process of integrating LLMs into the education industry, as well as the introduction of related technologies. Finally, we discuss the challenges and problems faced by LLMEdu, as well as prospects for future optimization of LLMEdu.

Related papers

• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• LLMs Meet Multimodal Generation and Editing: A Survey [89.76691959033323]
  This survey elaborates on multimodal generation and editing across various domains, comprising image, video, 3D, and audio. We summarize the notable advancements with milestone works in these fields and categorize these studies into LLM-based and CLIP/T5-based methods. We dig into tool-augmented multimodal agents that can leverage existing generative models for human-computer interaction.
  arXiv  Detail & Related papers  (2024-05-29T17:59:20Z)
• Exploring the landscape of large language models: Foundations, techniques, and challenges [8.042562891309414]
  The article sheds light on the mechanics of in-context learning and a spectrum of fine-tuning approaches. It explores how LLMs can be more closely aligned with human preferences through innovative reinforcement learning frameworks. The ethical dimensions of LLM deployment are discussed, underscoring the need for mindful and responsible application.
  arXiv  Detail & Related papers  (2024-04-18T08:01:20Z)
• Large Language Models for Education: A Survey and Outlook [69.02214694865229]
  We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education. Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
  arXiv  Detail & Related papers  (2024-03-26T21:04:29Z)
• ChatGPT Alternative Solutions: Large Language Models Survey [0.0]
  Large Language Models (LLMs) have ignited a surge in research contributions within this domain. Recent years have witnessed a dynamic synergy between academia and industry, propelling the field of LLM research to new heights. This survey furnishes a well-rounded perspective on the current state of generative AI, shedding light on opportunities for further exploration, enhancement, and innovation.
  arXiv  Detail & Related papers  (2024-03-21T15:16:50Z)
• Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
  We explore machine unlearning in the domain of large language models (LLMs) This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
  arXiv  Detail & Related papers  (2024-02-13T20:51:58Z)
• Taking the Next Step with Generative Artificial Intelligence: The Transformative Role of Multimodal Large Language Models in Science Education [13.87944568193996]
  Multimodal Large Language Models (MLLMs) are capable of processing multimodal data including text, sound, and visual inputs. This paper explores the transformative role of MLLMs in central aspects of science education by presenting exemplary innovative learning scenarios.
  arXiv  Detail & Related papers  (2024-01-01T18:11:43Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Large Language Models in Education: Vision and Opportunities [23.399139761508934]
  This article introduces the research background and motivation of large language models (LLMs) It then discusses the relationship between digital education and EduLLMs and summarizes the current research status of educational large models. Main contributions are the systematic summary and vision of the research background, motivation, and application of large models for education (LLM4Edu)
  arXiv  Detail & Related papers  (2023-11-22T05:04:20Z)
• A Survey on Multimodal Large Language Models [71.63375558033364]
  Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot. This paper aims to trace and summarize the recent progress of MLLMs.
  arXiv  Detail & Related papers  (2023-06-23T15:21:52Z)

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.