{"ID":2834025,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.11544","arxiv_id":"2601.11544","title":"Medication counseling with large language models: balancing flexibility and rigidity","abstract":"The introduction of large language models (LLMs) has greatly enhanced the capabilities of software agents. Instead of relying on rule-based interactions, agents can now interact in flexible ways akin to humans. However, this flexibility quickly becomes a problem in fields where errors can be disastrous, such as in a pharmacy context, but the opposite also holds true; a system that is too inflexible will also lead to errors, as it can become too rigid to handle situations that are not accounted for. Work using LLMs in a pharmacy context have adopted a wide scope, accounting for many different medications in brief interactions -- our strategy is the opposite: focus on a more narrow and long task. This not only enables a greater understanding of the task at hand, but also provides insight into what challenges are present in an interaction of longer nature. The main challenge, however, remains the same for a narrow and wide system: it needs to strike a balance between adherence to conversational requirements and flexibility. In an effort to strike such a balance, we present a prototype system meant to provide medication counseling while juggling these two extremes. We also cover our design in constructing such a system, with a focus on methods aiming to fulfill conversation requirements, reduce hallucinations and promote high-quality responses. The methods used have the potential to increase the determinism of the system, while simultaneously not removing the dynamic conversational abilities granted by the usage of LLMs. However, a great deal of work remains ahead, and the development of this kind of system needs to involve continuous testing and a human-in-the-loop. It should also be evaluated outside of commonly used benchmarks for LLMs, as these do not adequately capture the complexities of this kind of conversational system.","short_abstract":"The introduction of large language models (LLMs) has greatly enhanced the capabilities of software agents. Instead of relying on rule-based interactions, agents can now interact in flexible ways akin to humans. However, this flexibility quickly becomes a problem in fields where errors can be disastrous, such as in a ph...","url_abs":"https://arxiv.org/abs/2601.11544","url_pdf":"https://arxiv.org/pdf/2601.11544v1","authors":"[\"Joar Sabel\",\"Mattias Wingren\",\"Andreas Lundell\",\"Sören Andersson\",\"Sara Rosenberg\",\"Susanne Hägglund\",\"Linda Estman\",\"Malin Andtfolk\"]","published":"2025-12-02T13:50:03Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}