Helping languange models process spatial data using Langchain
The advent of ChatGPT and other Large Language Models in recent years has caused a surge in popular interest in Artificial Intelligence and its capabilities. Although Large Language Models may seem capable of an endless variety of tasks, there are still areas it struggles in such as the halluc...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175031 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The advent of ChatGPT and other Large Language Models in recent years has caused a surge
in popular interest in Artificial Intelligence and its capabilities. Although Large Language Models
may seem capable of an endless variety of tasks, there are still areas it struggles in such as the
hallucination problem or in its understanding of non-textual data, like geospatial data. This
project seeks to address this issue by exploring methodologies for Large Language Models to
interpret and use geospatial data more accurately, and to develop an easily operable workflow
that uses PostGIS and LangChain, among other technologies. The workflow will be grounded in
theoretical concepts like few-shot learning, which will be elaborated upon in this report. An
application user interface incorporating this workflow will be built in Flask, and it is hoped that
the results presented in this report will be useful for the future development of software wishing
to best utilize Large Language Models for processing geospatial data. |
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