Summary of What Can Llm Tell Us About Cities?, by Zhuoheng Li et al.
What can LLM tell us about cities?
by Zhuoheng Li, Yaochen Wang, Zhixue Song, Yuqi Huang, Rui Bao, Guanjie Zheng, Zhenhui Jessie Li
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper investigates the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. The researchers employ two methods: directly querying the LLM for target variable values and extracting explicit and implicit features from the LLM correlated with the target variable. The experiments reveal that LLMs embed a broad but varying degree of knowledge across global cities, with ML models trained on LLM-derived features consistently leading to improved predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are really smart computers that can learn lots of things about our world. This study looked at how well these computers know about different cities and regions around the globe. The scientists used two ways to test the computer’s knowledge: asking it direct questions and looking at what features it uses to understand those answers. They found out that the computer knows a lot about many places, but sometimes it doesn’t know things or gives silly answers when it doesn’t have enough information. This study shows that these computers can be super helpful for making decisions based on data. |
