用本地大模型从酒店评论中提取数据，构建可解释的模糊认知图。
LLM for the development of FCM
- 用Qwen2.5-32B模型从TripAdvisor评论中自动提取实体与量化信息。
- 基于未过滤文本训练出星型拓扑的模糊认知图，准确反映用户偏好。
- 验证了模型能预测评分与满意度间的关联，适合决策支持研究者。
本文探讨利用本地大语言模型开发模糊认知图（FCM）的方法。随着技术进步，本地大模型如Qwen2.5-32B已具备从文本中提取数量信息的能力：输入实体关键词后，模型可输出相关定量数据。这些数据被用于构建数据驱动的模糊认知图。实验中使用来自TripAdvisor的酒店评论作为原始数据，未经过滤直接输入模型，生成并训练了模糊认知图。针对希腊评论案例，构建出星型拓扑结构的FCM，揭示了用户的偏好模式。进一步通过外部验证发现，该模型能够将评论的星级评分（模型未参与推断的输出变量）与预测满意度相关联，验证了其有效性和可解释性。
原文摘要 · Abstract (English)
This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as prompt input and determine relevant quantitative data as the model output. In turn, this output can be utilized for the construction of a data driven fuzzy cognitive map. Hence, this implementation is achieved and then the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor. Furthermore, the extracted documents pass through the model unfiltered and then a fuzzy cognitive map is trained and evaluated. A case is made about Greek reviews where a star topology FCM is formed that indicates the preferences of the reviewers. Finally, external validation is performed to establish whether the fuzzy cognitive map can correlate the star rating of the review -an outcome outside the model's inference scope -with its predicted satisfaction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。