Language models have long been shown to embed geographical information in
their hidden representations. This line of work has recently been revisited by
extending this result to Large Language Models (LLMs). In this paper, we
propose to fill the gap between well-established and recent literature by
observing how geographical knowledge evolves when scaling language models. We
show that geographical knowledge is observable even for tiny models, and that
it scales consistently as we increase the model size. Notably, we observe that
larger language models cannot mitigate the geographical bias that is inherent
to the training data.

扩展大型语言模型 (LLM) 到研究结果，观察语言模型在扩展时地理知识是如何演变的，并显示较大的语言模型不能消除训练数据中固有的地理偏见。

自然语言模型中地理表现的缩放规律

On the Scaling Laws of Geographical Representation in Language Models

City-scale 3D point cloud is a promising way to express detailed and
complicated outdoor structures. It encompasses both the appearance and geometry
features of segmented city components, including cars, streets, and buildings,
that can be utilized for attractive applications such as user-interactive
navigation of autonomous vehicles and drones. However, compared to the
extensive text annotations available for images and indoor scenes, the scarcity
of text annotations for outdoor scenes poses a significant challenge for
achieving these applications. To tackle this problem, we introduce the
CityRefer dataset for city-level visual grounding. The dataset consists of 35k
natural language descriptions of 3D objects appearing in SensatUrban city
scenes and 5k landmarks labels synchronizing with OpenStreetMap. To ensure the
quality and accuracy of the dataset, all descriptions and labels in the
CityRefer dataset are manually verified. We also have developed a baseline
system that can learn encoded language descriptions, 3D object instances, and
geographical information about the city's landmarks to perform visual grounding
on the CityRefer dataset. To the best of our knowledge, the CityRefer dataset
is the largest city-level visual grounding dataset for localizing specific 3D
objects.

介绍了 CityRefer 数据集，该数据集是最大的城市级地理定位数据集，用于表示具体的 3D 物体，并开发了基线系统来执行视觉定位任务。

CityRefer：城市尺度点云数据的地理感知 3D 视觉引导数据集

CityRefer: Geography-aware 3D Visual Grounding Dataset on City-scale  Point Cloud Data

Extracting precise geographical information from textual contents is crucial
in a plethora of applications. For example, during hazardous events, a robust
and unbiased toponym extraction framework can provide an avenue to tie the
location concerned to the topic discussed by news media posts and pinpoint
humanitarian help requests or damage reports from social media. Early studies
have leveraged rule-based, gazetteer-based, deep learning, and hybrid
approaches to address this problem. However, the performance of existing tools
is deficient in supporting operations like emergency rescue, which relies on
fine-grained, accurate geographic information. The emerging pretrained language
models can better capture the underlying characteristics of text information,
including place names, offering a promising pathway to optimize toponym
recognition to underpin practical applications. In this paper, TopoBERT, a
toponym recognition module based on a one dimensional Convolutional Neural
Network (CNN1D) and Bidirectional Encoder Representation from Transformers
(BERT), is proposed and fine-tuned. Three datasets (CoNLL2003-Train,
Wikipedia3000, WNUT2017) are leveraged to tune the hyperparameters, discover
the best training strategy, and train the model. Another two datasets
(CoNLL2003-Test and Harvey2017) are used to evaluate the performance. Three
distinguished classifiers, linear, multi-layer perceptron, and CNN1D, are
benchmarked to determine the optimal model architecture. TopoBERT achieves
state-of-the-art performance (f1-score=0.865) compared to the other five
baseline models and can be applied to diverse toponym recognition tasks without
additional training.

本文提出了一种基于一维卷积神经网络（CNN1D）和来自 Transformer 的双向编码器表示（BERT）的地名识别模块 TopoBERT，通过 fine-tuned 的方式在三个数据集上得到最优结果，比其他五个基线模型表现更出色，可以应用于多种地名识别任务。

TopoBERT：基于 Fine-tuned BERT 的即插即用地名识别模块

TopoBERT: Plug and Play Toponym Recognition Module Harnessing Fine-tuned BERT

Recommender Systems (RSs) aim to model and predict the user preference while
interacting with items, such as Points of Interest (POIs). These systems face
several challenges, such as data sparsity, limiting their effectiveness. In
this paper, we address this problem by incorporating social, geographical, and
temporal information into the Matrix Factorization (MF) technique. To this end,
we model social influence based on two factors: similarities between users in
terms of common check-ins and the friendships between them. We introduce two
levels of friendship based on explicit friendship networks and high check-in
overlap between users. We base our friendship algorithm on users' geographical
activity centers. The results show that our proposed model outperforms the
state-of-the-art on two real-world datasets. More specifically, our ablation
study shows that the social model improves the performance of our proposed POI
recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms
of Precision@10, respectively.

该论文使用矩阵分解技术将社交、地理和时间信息纳入推荐系统中，增加了友谊算法和活动中心，从而提高了 POI 推荐系统的性能。实验结果表明，该模型在真实世界数据集上优于现有技术，精确率提高了 31% 和 14%。