Humans subconsciously engage in geospatial reasoning when reading articles.
We recognize place names and their spatial relations in text and mentally
associate them with their physical locations on Earth. Although pretrained
language models can mimic this cognitive process using linguistic context, they
do not utilize valuable geospatial information in large, widely available
geographical databases, e.g., OpenStreetMap. This paper introduces GeoLM, a
geospatially grounded language model that enhances the understanding of
geo-entities in natural language. GeoLM leverages geo-entity mentions as
anchors to connect linguistic information in text corpora with geospatial
information extracted from geographical databases. GeoLM connects the two types
of context through contrastive learning and masked language modeling. It also
incorporates a spatial coordinate embedding mechanism to encode distance and
direction relations to capture geospatial context. In the experiment, we
demonstrate that GeoLM exhibits promising capabilities in supporting toponym
recognition, toponym linking, relation extraction, and geo-entity typing, which
bridge the gap between natural language processing and geospatial sciences. The
code is publicly available at this https URL

GeoLM 是一个地理信息语言模型，通过连接文本语料库中的语言信息和来自地理数据库的地理信息，利用对比学习和遮蔽语言建模的方法，结合空间坐标嵌入机制来捕捉地理空间上下文，从而提升自然语言中的地理实体的理解能力。实验证明 GeoLM 在地名识别、地名链接、关系提取和地理实体类型等方面具有良好的能力，弥合了自然语言处理和地理空间科学之间的差距。

GeoLM：地理空间语言理解模型的增强

GeoLM: Empowering Language Models for Geospatially Grounded Language  Understanding

Despite the impressive performance of Large Language Models (LLM) for various
natural language processing tasks, little is known about their comprehension of
geographic data and related ability to facilitate informed geospatial
decision-making. This paper investigates the extent of geospatial knowledge,
awareness, and reasoning abilities encoded within such pretrained LLMs. With a
focus on autoregressive language models, we devise experimental approaches
related to (i) probing LLMs for geo-coordinates to assess geospatial knowledge,
(ii) using geospatial and non-geospatial prepositions to gauge their geospatial
awareness, and (iii) utilizing a multidimensional scaling (MDS) experiment to
assess the models' geospatial reasoning capabilities and to determine locations
of cities based on prompting. Our results confirm that it does not only take
larger, but also more sophisticated LLMs to synthesize geospatial knowledge
from textual information. As such, this research contributes to understanding
the potential and limitations of LLMs in dealing with geospatial information.

该研究通过探测具备预训练能力的大型语言模型对地理数据的理解程度和与此相关的促进地理空间决策的能力，通过三个实验验证结论，表明合成地理空间知识需要更大规模和更复杂的语言模型，并且对于处理地理空间信息，大型语言模型的潜力和局限性值得研究。

大型语言模型具备地理空间知识吗？

Are Large Language Models Geospatially Knowledgeable?

The task of textual geolocation - retrieving the coordinates of a place based
on a free-form language description - calls for not only grounding but also
natural language understanding and geospatial reasoning. Even though there are
quite a few datasets in English used for geolocation, they are currently based
on open-source data (Wikipedia and Twitter), where the location of the
described place is mostly implicit, such that the location retrieval resolution
is limited. Furthermore, there are no datasets available for addressing the
problem of textual geolocation in morphologically rich and resource-poor
languages, such as Hebrew. In this paper, we present the Hebrew Geo-Location
(HeGeL) corpus, designed to collect literal place descriptions and analyze
lingual geospatial reasoning. We crowdsourced 5,649 literal Hebrew place
descriptions of various place types in three cities in Israel. Qualitative and
empirical analysis show that the data exhibits abundant use of geospatial
reasoning and requires a novel environmental representation.

本文介绍了 Hebrew Geo-Location（HeGeL）语料库，它是用于采集字面意义上的地方描述和分析语言地理空间推断的，证明数据展示了丰富的地理空间推断使用，并需要一种新的环境表示。