A Conceptual Map for Exploring the Landscape of Large Language Models

Selecting and evaluating the most suitable Large Language Model (LLM) for a given task remains a significant challenge, particularly in domain-specific applications such as healthcare and legal research, which require models to provide transparency regarding training data, task specialization, and compliance with ethical standards. Despite the availability of open-source platforms for sharing models and datasets, challenges persist in accessing critical information. Incomplete metadata and inconsistent documentation hinder efficient model discovery, comparison, and adoption. In this paper, we introduce a simple conceptual map for LLMs, designed to assist researchers and practitioners in understanding the complex landscape of generative models. We provide a rationale for our modeling choices and a comprehensive description of the map in terms of four interconnected entities. Our primary objective is to provide all practitioners in the field—not just developers, but managers, testers, and other people who are part of producing and using technology—a clear terminology for expressing how to address the LLM landscape. A secondary objective is a call on industry stakeholders—including collaborative platforms and model providers—to enhance transparency and reproducibility in LLM research and deployment.