arXiv:2607.04983v1 Announce Type: cross 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 implementati
Advances in local large language models (LLMs) like Qwen2.5-32B are reaching a point where they can reliably extract quantitative data from text, making their application in data-driven modeling feasible.
The ability of local LLMs to construct fuzzy cognitive maps from textual data demonstrates a significant step towards autonomous AI systems capable of understanding and modeling complex systems directly from unstructured information.
Local LLMs are no longer just text generators but can act as data interpreters and model builders, potentially democratizing access to sophisticated analytical tools.
- · AI developers
- · Data scientists
- · Researchers
- · SaaS providers
- · Traditional data extraction services
Local LLMs can automate the creation of sophisticated analytical models based on unstructured text data.
This could lead to a proliferation of customized, context-aware analytical tools across various domains without reliance on large cloud-based models.
The enhanced autonomy of AI agents, combined with their ability to build complex models, could accelerate the development of truly autonomous decision-making systems.
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Read at arXiv cs.AI