G eo A gent: To Empower LLM s using Geospatial Tools for Address Standardization (2024)

Abstract: This paper presents a novel solution to tackle the challenges that posed by the abundance of non-standard addresses, which input by users in modern applications such as navigation maps, ride-hailing apps, food delivery platforms, and logistics services. These manually entered addresses often contain irregularities, such as missing information, spelling errors, colloquial descriptions, and directional offsets, which hinder address-related tasks like address matching and linking. To tackle these challenges, we propose GeoAgent, a new framework comprising two main components: a large language model (LLM) and a suite of geographical tools. By harnessing the semantic understanding capabilities of the LLM and integrating specific geospatial tools, GeoAgent incorporates spatial knowledge into address texts and achieves efficient address standardization. Further, to verify the effectiveness and practicality of our approach, we construct a comprehensive dataset of complex non-standard addresses, which fills the gaps in existing datasets and proves invaluable for training and evaluating the performance of address standardization models in this community. Experimental results demonstrate the efficacy of GeoAgent, showcasing substantial improvements in the performance of address-related models across various downstream tasks.

## introduction with the widespread using of navigation maps (e.g. google maps), ride-hailing apps (e.g. uber), food delivery platforms (e.g. uber eats), and logistics services (e.g. amazon logistics) in our daily life, a significant amount of user-entered addresses have been collected. however, these addresses often suffer from irregularities, such as missing addresses and spelling errors (figure 1(a) ), directional offset descriptions (figure 1 (b), colloquial descriptions (figure 1(c )) etc. handling these non-standard addresses presents considerable challenges for subsequent downstream tasks such as address matching @xcite and address linking @xcite . to address this issue, the task of address standardization @xcite is proposed to correct, complete, and normalize input address data, converting non-standard addresses into standard ones. actually, there are various possible ways to express an address. for example, in figure 1 , phrase a, b and c represent the same geographical location but are totally different in surface expression. this requires the model to possess relevant spatial knowledge and strong semantic understanding ability, so as to normalize these addresses to the standard one. conventional methods for address standardization often rely on character matching and rules, such as splitting address hierarchies based on address tree @xcite and then completing missing administrative region elements based on a hierarchical address database @xcite . unfortunately, these methods face limitations in handling fine-grained non-standard addresses, and shallow rules may not cover all sce-narios. in recent years, with the advancements in deep learning technology, researchers have explored the use of neural network models to tackle non-standard address issues @xcite . recently, many methods of pre-training in geographic text corpus have been proposed @xcite @xcite , or design models for specific tasks @xcite @xcite . however, such methods require a large amount of annotated data and are unable to handle common descriptive information (e.g., 20 meters southwest) in addresses. with the emergence of large language models (llms) like chatgpt, they have demonstrated powerful semantic understanding capabilities, leading to remarkable performance across multiple tasks. existing work shows that llms have some coarse-grained geographic knowledge, but it still suffer from the following problems according to our observations: 1) spatial similarity issues: llms excel in processing natural language texts and understanding their semantics. however, address information processing involves spatial similarity issues. geographically close addresses may have significant textual differences, such as "no. 323 songhan road, baoshan district" and "no. 861 guofan road, yangpu district". although the two addresses are literally completely different, they are actually very close, located on opposite sides of an intersection. to tackle such cases, the model needs spatial knowledge to discern spatial correlations. unfortunately, existing llms often lack geographic and address-related fine-grained knowledge, resulting in unsatisfactory results when dealing with addressing-related problems. 2) changes in geographic spatial knowledge: geographic information is subject to frequent changes, with businesses, companies, and establishments experiencing relocations, closures, and other transformations over time. large language models store knowledge in parameters, making it difficult for them to adapt to such knowledge changes. it requires substantial training costs to update these parameters, which makes it challengeable to keep the model up-to-date with the realworld changes in address information 3) deficiencies in precise numerical calculations: existing work indicates that llms have deficiencies in performing precise numerical calculations @xcite . this limitation makes it challengeable for them to handle non-standard addresses that involve directional shifts (e.g. figure 1(b) ). to tackle the above problems, inspired by the latest research on llm agent @xcite , we propose geoagent, an innovative framework that combines a llm with geospatial tools. by leveraging the language understanding and decision-making capabilities of llms to interact with geospatial tools, geoagent effectively processes non-standard addresses. by introducing an address knowledge base, fine-grained geospatial knowledge can be obtained. to maintain geospatial knowledge efficiently, we store it in an external vector database. this approach significantly reduces costs compared to re-training the model every time when the knowledge needs updating. furthermore, our method uses spatial computing tools to achieve accurate spatial offset calculations. recognizing the lack of a dataset that contains various non-standard addresses, especially those with descriptive information, we construct a comprehensive dataset for providing non-standard addresses for this task. our dataset covers complex non-standard address linking, address standardization, and geocoding, which effectively fills the gaps in existing datasets. we have conducted extensive experiments on both our dataset and existing datasets, demonstrating that geoagent's standardized processing significantly enhances the performance of address-related models in downstream tasks. to account for the importance of text position in address text evaluation, we propose a new standardized metric called georouge, which provides a more comprehensive and relevant evaluation for address standardized. our major contributions are highlighted as follows: cake shop, 20 meters southwest of the intersection of guoquan north road and guoxue road, yangpu district, shanghai api 1: address mapping poi database geocoding model api 2: geographical offset correction api 3: road information non-standard address: normalization address tianxia cakeshop, 1580 guoquan north road, yangpu district, shanghai input: address example: the intersection of guoquan north …. output: s2 token example: 35b27304a89f input: api 1 output& description information example:(southwest,20 ),35b27304a89f output: s2 token example: 35b273035897 offset computing input: road name example: guoquan north road opm map output: road information example: (sencondary highway, direction…) instruct: for the input address, generate a series of tool calls to standardize the address llm output：<@xmath0: identify the standard address part of the address, that is, the content of the standard address does not contain geographical location description information (such as: east, northwest, nearby opposite, etc.) llm output：< the intersection of guoquan north road and guoxue road, yangpu district, @xmath1: identify the poi name in this address llm output: cake shop (poi name, address location) -> normalization address stage #1: llm planning stage #2: task execution stage #3:address text normalization query address1: cake….,yangpu,shanghai address2: xx university,…, shanghai candidate ：yes✔ no❌ query cake….,yangpu,shanghai output (121,50979,31.30102) query cake…,yangpu,shanghai output address base query cake…,20 meters southewest of …,shanghai standard address tianxia cake shop, 1480 guoquan north road, yangpu district, shanghai) geocoding address link address match address standardization (a) geoagent design (b) geography tools (c) downstream tasks of experiments. the results demonstrate significant improvements in various tasks when using the normalized data. ## geoagent before diving into technical details, we formally define the address standardization task below: address standardization. we define the address standardization task as the problem of going from a non-standard address s to a standard address s * . the standard address refers to the address expression that conforms to the address writing rules. taking the chinese scenario as an example, a standard city address expression should be "province -city -district -street -street number -poi name" (although our proposed approach is language-independent, we provide an analysis of different language implementations details in the appendix e). the core architecture of our framework is shown in figure 2 , which consists of three main steps: (1) task planning. given an address s, the model generates a series of task execution sequences. splitting the address standardization task into smaller tasks. (2) task execution. execute and return results based on the sequence of tasks are generated by the llm. ( 3 ) address text standardization. according to the results of the previous task, the address text processing process is executed to obtain the standardized address s * . the resulting standardized address is the output of the geoagent. ## experiments in this section, we present the results of the geoagent standardized address on four address-related tasks (address matching, geocoding, address stan- task train dev test geocoding 7540k 76k 76k address linking 175k 4k 4k address standardization 175k 4k 4k ## conclusion in conclusion, this paper introduces geoagent, a novel solution designed to address the challenges posed by non-standard addresses frequently input by users in modern applications. we utilize llm task planning ability and tool utilization ability to make up for llm lack of fine-grained spatial knowledge and accurate spatial calculation prob-lems, leading to efficient address standardization. additionally, we create an extensive dataset of complex non-standard addresses, which bridges the gaps in existing datasets. the experimental results demonstrate the effectiveness of geoagent, as it showcases significant enhancements in the performance of address-related models across various downstream tasks.