In the rapidly evolving landscape of Natural Language Processing (NLP), Large
Language Models (LLMs) have emerged as powerful tools for many tasks, such as
extracting valuable insights from vast amounts of textual data. In this study,
we conduct a comparative analysis of LLMs for the extraction of travel customer
needs from TripAdvisor posts. Leveraging a diverse range of models, including
both open-source and proprietary ones such as GPT-4 and Gemini, we aim to
elucidate their strengths and weaknesses in this specialized domain. Through an
evaluation process involving metrics such as BERTScore, ROUGE, and BLEU, we
assess the performance of each model in accurately identifying and summarizing
customer needs. Our findings highlight the efficacy of opensource LLMs,
particularly Mistral 7B, in achieving comparable performance to larger closed
models while offering affordability and customization benefits. Additionally,
we underscore the importance of considering factors such as model size,
resource requirements, and performance metrics when selecting the most suitable
LLM for customer needs analysis tasks. Overall, this study contributes valuable
insights for businesses seeking to leverage advanced NLP techniques to enhance
customer experience and drive operational efficiency in the travel industry.

通过比较分析不同的大型语言模型，尤其是开源的 Mistral 7B，我们揭示了它们在旅行客户需求分析任务中的优势和劣势，为企业利用先进的自然语言处理技术提升客户体验和推动运营效率方面提供了有价值的见解。