Summary of Understanding Survey Paper Taxonomy About Large Language Models Via Graph Representation Learning, by Jun Zhuang et al.
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning
by Jun Zhuang, Casey Kennington
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 proposes a method to automatically categorize Large Language Model (LLM) survey papers using a taxonomy. The researchers collect metadata from 144 LLM survey papers and explore three paradigms to classify them within the taxonomy. The study finds that leveraging graph structure information on co-category graphs can outperform pre-trained language models in two paradigms: fine-tuning and zero-shot/few-shot classifications. The results show that the model surpasses an average human recognition level, with weak-to-strong generalization having potential for improving classification accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to organize lots of information about Large Language Models (LLMs). It’s hard to keep up with all the new research and models coming out! To make it easier, this study creates a way to automatically sort survey papers into categories. They looked at 144 papers and found that by using special graph structures, they could do better than just using language models alone. This is important because it helps us understand how to use LLMs in different ways. |
Keywords
* Artificial intelligence * Classification * Few shot * Fine tuning * Generalization * Large language model * Zero shot
