Summary of Are Large Language Models a Good Replacement Of Taxonomies?, by Yushi Sun et al.
Are Large Language Models a Good Replacement of Taxonomies?
by Yushi Sun, Hao Xin, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 The study investigates whether large language models (LLMs) can replace traditional knowledge graphs, focusing on their performance in understanding schema-based knowledge. The paper highlights the limitations of previous studies, which only evaluated LLMs’ performance on general knowledge, leaving doubts about their capabilities in nuanced knowledge areas. To address this gap, the authors propose TaxoGlimpse, a benchmark that evaluates the performance of 18 state-of-the-art LLMs across ten representative taxonomies, from common to specialized domains, at different levels of entities from root to leaf. The results show that LLMs struggle to capture knowledge in specialized taxonomies and leaf-level entities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are amazing machines that can answer questions about many things. But do they know as much as we think? Researchers wondered if these models could replace the way we organize information, like a big tree with branches and leaves. They built a special test to see how well the models understand this structure. The results show that while the models are great at answering general questions, they struggle when it comes to very specific information. |
