Large Language Model-Driven Structured Output: A Comprehensive Benchmark and Spatial Data Generation Framework
Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this p...
Saved in:
| Main Authors: | Diya Li, Yue Zhao, Zhifang Wang, Calvin Jung, Zhe Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/13/11/405 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
From FOSS to profit: Digital spatial technologies and the mode of production
by: Dillon Mahmoudi, et al.
Published: (2024-12-01) -
pyJSON Schema Loader and JSON Editor: A tool for file-based metadata management
by: Nick Plathe, et al.
Published: (2024-12-01) -
PreparedLLM: effective pre-pretraining framework for domain-specific large language models
by: Zhou Chen, et al.
Published: (2024-10-01) -
τJOWL: A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data
by: Zouhaier Brahmia, et al.
Published: (2022-12-01) -
TCM-GPT: Efficient pre-training of large language models for domain adaptation in Traditional Chinese Medicine
by: Guoxing Yang, et al.
Published: (2024-01-01)