利用大语言模型进行用药咨询：在灵活性与严谨性之间取得平衡
Medication counseling with large language models: balancing flexibility and rigidity
AI总结：
本文提出了一种利用大语言模型在药房环境中提供用药咨询的原型系统，旨在平衡对话要求与灵活性，减少幻觉并提高响应质量。
摘要：
大语言模型（LLMs）的引入极大地增强了软件代理的能力。与依赖规则交互不同，代理现在可以像人类一样灵活地交互。然而，在错误可能导致灾难的领域，如药房环境中，这种灵活性很快会成为问题。相反，过于僵化的系统也会导致错误，因为它可能无法处理未被考虑的情况。在药房环境中使用LLMs的工作通常采用广泛范围，涵盖许多不同药物的简短交互——我们的策略相反：专注于更狭窄和长时间的任务。这不仅使任务本身有了更深入的理解，还提供了关于更长交互中存在哪些挑战的见解。然而，对于狭窄和广泛系统来说，主要挑战仍然相同：需要在遵守对话要求和灵活性之间取得平衡。为了实现这种平衡，我们提出了一个原型系统，旨在在兼顾这两个极端的同时提供用药咨询。我们还涵盖了构建此类系统的 设计，重点是旨在满足对话要求、减少幻觉并促进高质量响应的方法。所使用的方法有潜力提高系统的确定性，同时不削弱LLMs使用所带来的动态对话能力。然而，仍有大量工作需要完成，此类系统的开发需要持续测试和人机协同。它还应在常用的LLM基准之外进行评估，因为这些基准无法充分捕捉此类对话系统的复杂性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
