Summary of Tourllm: Enhancing Llms with Tourism Knowledge, by Qikai Wei et al.
TourLLM: Enhancing LLMs with Tourism Knowledge
by Qikai Wei, Mingzhi Yang, Jinqiang Wang, Wenwei Mao, Jiabo Xu, Huansheng Ning
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses a significant challenge in natural language processing (NLP), specifically in the tourism domain. Large language models (LLMs) have shown great promise, but their performance is limited by the lack of domain-specific knowledge. To bridge this gap, the authors created Cultour, a supervised fine-tuning dataset for culture and tourism, comprising three parts: tourism knowledge base QA data, travelogues data, and tourism diversity QA data. The proposed TourLLM model is a Qwen-based architecture fine-tuned with Cultour to generate high-quality information about attractions and travel planning. To evaluate the model’s performance, both automatic and human evaluation methods were employed, including a novel human evaluation criterion called CRA (Consistency, Readability, Availability). The results demonstrate the effectiveness of TourLLM in generating accurate and informative responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about helping machines like Google Assistant or Alexa provide better information for people planning trips. Right now, these AI systems are not very good at giving advice on tourist attractions because they don’t have enough knowledge about different places to visit. To fix this problem, the authors created a special dataset called Cultour that has lots of questions and answers about tourism. They then used this dataset to train a new AI model called TourLLM that can generate helpful information for people planning their trips. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge base » Natural language processing » Nlp » Supervised
