Large Language Models (LLMs) have emerged as leading tools in Natural Language Processing (NLP) due to their exceptional performance across various tasks. The advent of open-source models such as Llama [1] from Meta, Gemma [2] from Google, and Qwen [3] from Alibaba has significantly enhanced public access to advanced LLMs. Additionally, low-cost techniques for LLM fine-tuning, such as Low-rank Adaptation (LoRA) [4], have enabled the fine-tuning of these models on consumer-grade hardware, thereby accelerating their development and adoption. LLMs are now utilized in a wide array of applications, ranging from personal assistants, i.e., ChatGPT, to specialized tasks in diverse domains. In the financial sector, BloombergGPT [5], a proprietary LLM trained from the ground up with an infusion of financial data, has demonstrated superior performance on financial benchmarks compared to other models in the market.

Answer set programming (ASP) has emerged as an approach to declarative problem solving based on the stable model semantics for logic programs. The basic idea is to represent a computational problem by a logic program, formulating constraints in terms of rules, such that its answer sets correspond to problem solutions. Compact problem representations take advantage of genuine modeling features of ASP, including (first-order) variables, negation by default, and recursion. In this article, we demonstrate the ASP methodology on two example scenarios, illustrating basic as well as advanced modeling and solving concepts.