Author ORCID Identifier
Jose Tupayachi https://orcid.org/0000-0001-7334-8444
Xueping Li https://orcid.org/0000-0003-1990-0159
Document Type
Article
Publication Date
10-2024
DOI
https://doi.org/10.1145/3681772.3698217
Abstract
We present a pilot study exploring the potential of Large Language Models (LLMs) to interface with application programming interfaces through logical instructions, specifically within the domain of Geographic Question Answering for route optimization. This study employs a Continuous Retrieval-Augmented Generation approach combined with fine-tuned LLMs, featuring customized node-based storage and vector search retrieval. We also provide a comparative analysis of the method’s effectiveness and adaptability in handling diverse textual queries.
Recommended Citation
Jose Tupayachi and Xueping Li. 2024. Conversational Geographic Question Answering for Route Optimization: An LLM and Continuous Retrieval- Augmented Generation Approach . In 17th ACM SIGSPATIAL International Workshop on Computational Transportation Science GenAI and Smart Mobility Session (IWCTS’24), October 29-November 1 2024, Atlanta, GA, USA. ACM, Seattle, WA, USA, 4 pages. https://doi.org/10.1145/3681772.3698217
Submission Type
Publisher's Version