Optimizing and Fine-tuning Large Language Model for Urban Renewal

Understand

This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal.

• It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks.
• Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal.
• By guiding the LLM to automatically generate QA data based on prompt words and given text, it is possible to quickly obtain datasets in the urban renewal field and provide data support for the fine-tuning training of LLMs.