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.

翻译：在自然语言处理（NLP）快速发展的背景下，大型语言模型（LLMs）已成为从海量文本数据中提取有价值信息的强大工具。本研究对基于TripAdvisor帖子的旅游客户需求提取任务中使用的LLMs进行了比较分析。通过利用涵盖开源与专有模型（如GPT-4和Gemini）的多样化模型，我们旨在阐明它们在该专业领域中的优缺点。采用BERTScore、ROUGE和BLEU等评估指标，我们评估了各模型在准确识别和总结客户需求方面的性能。研究结果表明，开源LLMs（尤其是Mistral 7B）在实现与大型封闭模型相当性能的同时，兼具成本效益和定制化优势。此外，我们强调了在选择最适合客户需求分析任务的LLM时，需综合考虑模型规模、资源需求及性能指标等因素。总体而言，本研究为寻求利用先进NLP技术提升旅游行业客户体验和运营效率的企业提供了重要见解。