Automate Knowledge Concept Tagging on Math Questions with LLMs

• URL: http://arxiv.org/abs/2403.17281v1
• Date: Tue, 26 Mar 2024 00:09:38 GMT
• Title: Automate Knowledge Concept Tagging on Math Questions with LLMs
• Authors: Hang Li, Tianlong Xu, Jiliang Tang, Qingsong Wen,
• Abstract summary: Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications. Traditionally, these annotations have been conducted manually with help from pedagogical experts. In this paper, we explore the automating the tagging task using Large Language Models (LLMs)
• Score: 48.5585921817745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge concept tagging for questions plays a crucial role in contemporary intelligent educational applications, including learning progress diagnosis, practice question recommendations, and course content organization. Traditionally, these annotations have been conducted manually with help from pedagogical experts, as the task requires not only a strong semantic understanding of both question stems and knowledge definitions but also deep insights into connecting question-solving logic with corresponding knowledge concepts. In this paper, we explore automating the tagging task using Large Language Models (LLMs), in response to the inability of prior manual methods to meet the rapidly growing demand for concept tagging in questions posed by advanced educational applications. Moreover, the zero/few-shot learning capability of LLMs makes them well-suited for application in educational scenarios, which often face challenges in collecting large-scale, expertise-annotated datasets. By conducting extensive experiments with a variety of representative LLMs, we demonstrate that LLMs are a promising tool for concept tagging in math questions. Furthermore, through case studies examining the results from different LLMs, we draw some empirical conclusions about the key factors for success in applying LLMs to the automatic concept tagging task.

Related papers

• Knowledge Tagging with Large Language Model based Multi-Agent System [17.53518487546791]
  This paper investigates the use of a multi-agent system to address the limitations of previous algorithms. We highlight the significant potential of an LLM-based multi-agent system in overcoming the challenges that previous methods have encountered.
  arXiv  Detail & Related papers  (2024-09-12T21:39:01Z)
• Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever [48.5585921817745]
  Large Language Models (LLMs) are used to automate the knowledge tagging task. We show the strong performance of zero- and few-shot results over math questions knowledge tagging tasks. By proposing a reinforcement learning-based demonstration retriever, we successfully exploit the great potential of different-sized LLMs.
  arXiv  Detail & Related papers  (2024-06-19T23:30:01Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• Reinforcement Learning Problem Solving with Large Language Models [0.0]
  Large Language Models (LLMs) have an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of Natural Language Processing (NLP) tasks. This has also facilitated a more accessible paradigm of conversation-based interactions between humans and AI systems to solve intended problems. We show the practicality of our approach through two detailed case studies for "Research Scientist" and "Legal Matter Intake"
  arXiv  Detail & Related papers  (2024-04-29T12:16:08Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• From Understanding to Utilization: A Survey on Explainability for Large Language Models [27.295767173801426]
  This survey underscores the imperative for increased explainability in Large Language Models (LLMs) Our focus is primarily on pre-trained Transformer-based LLMs, which pose distinctive interpretability challenges due to their scale and complexity. When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement.
  arXiv  Detail & Related papers  (2024-01-23T16:09:53Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [91.30946119104111]
  We show that large language models (LLMs) possess unwavering confidence in their capabilities to respond to questions. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We also find that LLMs have a propensity to rely on the provided retrieval results when formulating answers.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [48.70557995528463]
  This guide aims to provide researchers and practitioners with valuable insights and best practices for working with Large Language Models. We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios.
  arXiv  Detail & Related papers  (2023-04-26T17:52:30Z)

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.