Fine-Tuning Small Language Models (SLMs) for Autonomous Web-based Geographical Information Systems (AWebGIS)

TL;DR

This paper demonstrates that fine-tuned small language models can effectively perform geospatial tasks in web browsers, offering privacy-preserving, scalable, and accurate solutions for autonomous web-based GIS systems.

Contribution

It introduces a fully autonomous offline approach using a fine-tuned T5-small model for AWebGIS, outperforming cloud-based and classical machine learning methods in accuracy.

Findings

SLM-based approach achieved 0.93 exact match accuracy

Client-side processing reduces server load and enhances privacy

SLM approach outperforms cloud-based and classical classifiers

Abstract

Autonomous web-based geographical information systems (AWebGIS) aim to perform geospatial operations from natural language input, providing intuitive, intelligent, and hands-free interaction. However, most current solutions rely on cloud-based large language models (LLMs), which require continuous internet access and raise users' privacy and scalability issues due to centralized server processing. This study compares three approaches to enabling AWebGIS: (1) a fully-automated online method using cloud-based LLMs (e.g., Cohere); (2) a semi-automated offline method using classical machine learning classifiers such as support vector machine and random forest; and (3) a fully autonomous offline (client-side) method based on a fine-tuned small language model (SLM), specifically T5-small model, executed in the client's web browser. The third approach, which leverages SLMs, achieved the highest accuracy among all methods, with an exact matching accuracy of 0.93, Levenshtein similarity of 0.99, and recall-oriented understudy for gisting evaluation ROUGE-1 and ROUGE-L scores of 0.98. Crucially, this client-side computation strategy reduces the load on backend servers by offloading processing to the user's device, eliminating the need for server-based inference. These results highlight the feasibility of browser-executable models for AWebGIS solutions.