[2603.22519] LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface

Computer Science > Software Engineering arXiv:2603.22519 (cs) [Submitted on 23 Mar 2026] Title:LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface Authors:Michael Hind, Basel Shbita, Bo Wu, Farhan Ahmed, Chad DeLuca, Nathan Fulton, David Cox, Dan Gutfreund View a PDF of the paper titled LLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface, by Michael Hind and 7 other authors View PDF HTML (experimental) Abstract:Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the information conveyed to an LLM often has a richer structure and semantics, which is not conveyed in a string. For example, most prompts contain both instructions ("Summarize this paper into a paragraph") and data (the paper to summarize), but these are usually not distinguished when passed to the model. This can lead to model confusion and security risks, such as prompt injection attacks. This work addresses this shortcoming by introducing an LLM-native mark-up language, LLMON (LLM Object Notation, pronounced "Lemon"), that enables the structure and semantic metadata of the text to be communicated in a natural way to an LLM. This information can then be used during model training, model prompting, and inference implementation, leading to improvements in model accuracy, safety, and security. This is analogous to how programming language types c...