  Recently, large language models (LLMs) have demonstrated their effectiveness
in various natural language processing (NLP) tasks. However, the lack of
tourism knowledge limits the performance of LLMs in tourist attraction
presentations and travel planning. To address this challenge, we constructed a
supervised fine-tuning dataset for the culture and tourism domain, named
Cultour. This dataset consists of three parts: tourism knowledge base QA data,
travelogues data, and tourism diversity QA data. Additionally, we propose
TourLLM, a Qwen-based model supervised fine-tuned with Cultour, to improve the
quality of the information provided about attractions and travel planning. To
evaluate the performance of TourLLM, we employed both automatic and human
evaluation, and we proposed a human evaluation criterion named CRA
(Consistency, Readability, Availability). The experimental results demonstrate
the effectiveness of the responses generated by the TourLLM. Our proposed
Cultour is accessible at https://github.com/mrweiqk/Cultour.
