What can LLM tell us about cities?

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.

FGN: Fusion Glyph Network for Chinese Named Entity Recognition

Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information, which is often overlooked. We propose the FGN, Fusion Glyph Network for Chinese NER. This method may offer glyph information for fusion representation learning with BERT. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs. (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.