A Conceptual Map for Exploring the Landscape of Large Language Models

Abstract

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.

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