Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects

• URL: http://arxiv.org/abs/2412.04185v1
• Date: Thu, 05 Dec 2024 14:24:07 GMT
• Title: Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects
• Authors: Dominic Lohr, Marc Berges, Abhishek Chugh, Michael Kohlhase, Dennis Müller,
• Abstract summary: Recent progress in generative natural language models has opened up new potential in the generation of educational content. This paper explores the potential of large language models for generating computer science questions that are sufficiently annotated for automatic learner model updates.
• Score: 2.1845291030915974
• License:
• Abstract: Background: Over the past few decades, the process and methodology of automated question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content. Objectives: This paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course, and address the cognitive dimension understand. Methods: Unlike previous attempts that might use basic methods like ChatGPT, our approach involves more targeted strategies such as retrieval-augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning objects. Results and Conclusions: Our results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content.

Related papers

• Learning Task Representations from In-Context Learning [73.72066284711462]
  Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning. We introduce an automated formulation for encoding task information in ICL prompts as a function of attention heads. We show that our method's effectiveness stems from aligning the distribution of the last hidden state with that of an optimally performing in-context-learned model.
  arXiv  Detail & Related papers  (2025-02-08T00:16:44Z)
• Evolving Knowledge Distillation with Large Language Models and Active Learning [46.85430680828938]
  Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. Previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. We propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models.
  arXiv  Detail & Related papers  (2024-03-11T03:55:24Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• SINC: Self-Supervised In-Context Learning for Vision-Language Tasks [64.44336003123102]
  We propose a framework to enable in-context learning in large language models. A meta-model can learn on self-supervised prompts consisting of tailored demonstrations. Experiments show that SINC outperforms gradient-based methods in various vision-language tasks.
  arXiv  Detail & Related papers  (2023-07-15T08:33:08Z)
• Prototyping the use of Large Language Models (LLMs) for adult learning content creation at scale [0.6628807224384127]
  This paper presents an investigation into the use of Large Language Models (LLMs) in asynchronous course creation. We developed a course prototype leveraging an LLM, implementing a robust human-in-the-loop process. Initial findings indicate that taking this approach can indeed facilitate faster content creation without compromising on accuracy or clarity.
  arXiv  Detail & Related papers  (2023-06-02T10:58:05Z)
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy [164.83371924650294]
  We show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge. Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.
  arXiv  Detail & Related papers  (2023-05-24T16:17:36Z)
• Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text [3.114960935006655]
  This paper aims to improve the state of the art of automatic construction and completion of knowledge graphs from text. In this context, one emerging paradigm is in-context learning where a language model is used as it is with a prompt.
  arXiv  Detail & Related papers  (2023-05-15T17:10:19Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)
• Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers [5.968260239320591]
  Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG.
  arXiv  Detail & Related papers  (2022-03-11T13:47:08Z)

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.