A Roadmap for Tamed Interactions with Large Language Models

• URL: http://arxiv.org/abs/2510.24819v1
• Date: Tue, 28 Oct 2025 13:46:07 GMT
• Title: A Roadmap for Tamed Interactions with Large Language Models
• Authors: Vincenzo Scotti, Jan Keim, Tobias Hey, Andreas Metzger, Anne Koziolek, Raffaela Mirandola,
• Abstract summary: We are witnessing a bloom of AI-powered software driven by Large Language Models (LLMs)<n>Although the applications of these LLMs are impressive and seemingly countless, their robustness hinders adoption.<n>With LSL, we aim to address the limitations above by exploring ways to control LLM outputs, enforce structure in interactions, and integrate these aspects with verification, validation, and explainability.
• Score: 5.133046277847902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We are witnessing a bloom of AI-powered software driven by Large Language Models (LLMs). Although the applications of these LLMs are impressive and seemingly countless, their unreliability hinders adoption. In fact, the tendency of LLMs to produce faulty or hallucinated content makes them unsuitable for automating workflows and pipelines. In this regard, Software Engineering (SE) provides valuable support, offering a wide range of formal tools to specify, verify, and validate software behaviour. Such SE tools can be applied to define constraints over LLM outputs and, consequently, offer stronger guarantees on the generated content. In this paper, we argue that the development of a Domain Specific Language (DSL) for scripting interactions with LLMs using an LLM Scripting Language (LSL) may be key to improve AI-based applications. Currently, LLMs and LLM-based software still lack reliability, robustness, and trustworthiness, and the tools or frameworks to cope with these issues suffer from fragmentation. In this paper, we present our vision of LSL. With LSL, we aim to address the limitations above by exploring ways to control LLM outputs, enforce structure in interactions, and integrate these aspects with verification, validation, and explainability. Our goal is to make LLM interaction programmable and decoupled from training or implementation.

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