**arXiv ID:** 2607.04983 **Authors:** Alexis Kafantaris **Published:** 2026-07-06T12:18:54Z **Abstract:** 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 ...
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# LLM for the development of FCM
**arXiv ID:** 2607.04983
**Authors:** Alexis Kafantaris
**Published:** 2026-07-06T12:18:54Z
**Abstract:**
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.
## Skill Description
This skill is generated from the arXiv paper: LLM for the development of FCM (2607.04983).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2607.04983](http://arxiv.org/abs/2607.04983v2)
