Summary of Knowledge Tagging System on Math Questions Via Llms with Flexible Demonstration Retriever, by Hang Li et al.
Knowledge Tagging System on Math Questions via LLMs with Flexible Demonstration Retriever
by Hang Li, Tianlong Xu, Jiliang Tang, Qingsong Wen
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores automating knowledge tagging for questions using Large Language Models (LLMs). The task is crucial for intelligent educational applications like learning progress diagnosis, practice question recommendations, and course content organization. Prior methods have struggled with strong domain knowledge and complicated concept definitions, but LLMs demonstrate great potential in conquering these challenges. Specifically, the paper shows strong performance of zero- and few-shot results over math questions knowledge tagging tasks, making LLMs a promising solution for this problem. Additionally, the authors propose a reinforcement learning-based demonstration retriever to further improve performance while maintaining efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier to match questions with related answers using special computers called Large Language Models (LLMs). Right now, experts need to do this work by hand, but LLMs can help automate the process. The paper shows that these computer models are really good at matching math problems with their corresponding answers even when they don’t have much training data. This is important because it could make learning more efficient and personalized. |
Keywords
» Artificial intelligence » Few shot » Reinforcement learning
