What can LLM tell us about cities?
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929605595627520 |
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| author | Li, Zhuoheng Wang, Yaochen Song, Zhixue Huang, Yuqi Bao, Rui Zheng, Guanjie Li, Zhenhui Jessie |
| author_facet | Li, Zhuoheng Wang, Yaochen Song, Zhixue Huang, Yuqi Bao, Rui Zheng, Guanjie Li, Zhenhui Jessie |
| contents | This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. We 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. Our 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. Additionally, we observe that LLMs demonstrate a certain level of knowledge across global cities on all continents, but it is evident when they lack knowledge, as they tend to generate generic or random outputs for unfamiliar tasks. These findings suggest that LLMs can offer new opportunities for data-driven decision-making in the study of cities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16791 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | What can LLM tell us about cities? Li, Zhuoheng Wang, Yaochen Song, Zhixue Huang, Yuqi Bao, Rui Zheng, Guanjie Li, Zhenhui Jessie Computation and Language Artificial Intelligence Information Retrieval This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. We 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. Our 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. Additionally, we observe that LLMs demonstrate a certain level of knowledge across global cities on all continents, but it is evident when they lack knowledge, as they tend to generate generic or random outputs for unfamiliar tasks. These findings suggest that LLMs can offer new opportunities for data-driven decision-making in the study of cities. |
| title | What can LLM tell us about cities? |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2411.16791 |