Normative requirements specify social, legal, ethical, empathetic, and cultural (SLEEC) norms that must be observed by a system. To support the identification of SLEEC requirements, numerous standards and regulations have been developed. These requirements are typically defined by stakeholders in the non-technical sys- tem with diverse expertise (e.g., ethicists, lawyers, social scientists). Hence, ensuring their consistency and manag- ing the requirement elicitation process are complex and error-prone tasks. Recent research has addressed this challenge using domain-specific languages to specify normative requirements as rules, whose consistency can then be analyzed with formal methods. Nevertheless, these approaches often present the results from formal verification tools in a way that is inaccessible to non-technical users. This hinders understanding and makes the iterative process of eliciting and validating these requirements inefficient in terms of both time and effort. To address this problem, we introduce SLEEC-LLM, a tool that uses large language models (LLMs) to pro- vide natural-language interpretations for model-checking counterexamples corresponding to SLEEC rule inconsis- tencies. SLEEC-LLM improves the efficiency and explainability of normative requirements elicitation and consistency analysis. To demonstrate its effectiveness, we summarise its use in two real-world case studies involving non-technical stakeholders.