TourLLM: Enhancing LLMs with Tourism Knowledge

• URL: http://arxiv.org/abs/2407.12791v1
• Date: Tue, 18 Jun 2024 09:15:46 GMT
• Title: TourLLM: Enhancing LLMs with Tourism Knowledge
• Authors: Qikai Wei, Mingzhi Yang, Jinqiang Wang, Wenwei Mao, Jiabo Xu, Huansheng Ning,
• Abstract summary: We construct 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. We propose TourLLM, a Qwen-based model supervised fine-tuned with Cultour, to improve the quality of the information about attractions and travel planning.
• Score: 3.034710104407876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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