标题： 基于大语言模型的配置合成需要消歧 显示英文标题

标题： LLM-Based Config Synthesis requires Disambiguation

摘要： 除了幻觉之外，使用大语言模型进行程序合成的另一个问题是用户意图的模糊性。 我们在网络环境中说明了基于大语言模型的路由图和ACL的增量配置合成中的模糊性问题。 这些结构在报头空间中经常重叠，使得大语言模型无法在没有用户交互的情况下推断出操作的相对优先级。 在大型云环境中的测量结果显示具有数百个重叠的复杂ACL，表明模糊性是一个真实的问题。 我们提出了一种原型系统Clarify，该系统通过一个称为消歧器的新模块来增强大语言模型，以帮助获取用户意图。 在一个小型合成工作负载上，Clarify在消歧之后增量地合成路由策略，然后对其进行验证。 当更新的意图可以被大语言模型正确合成时，我们对模糊性的处理更具普遍适用性，但它们的集成是模糊的，可能导致不同的全局行为。 显示英文摘要

摘要： Beyond hallucinations, another problem in program synthesis using LLMs is ambiguity in user intent. We illustrate the ambiguity problem in a networking context for LLM-based incremental configuration synthesis of route-maps and ACLs. These structures frequently overlap in header space, making the relative priority of actions impossible for the LLM to infer without user interaction. Measurements in a large cloud identify complex ACLs with 100's of overlaps, showing ambiguity is a real problem. We propose a prototype system, Clarify, which uses an LLM augmented with a new module called a Disambiguator that helps elicit user intent. On a small synthetic workload, Clarify incrementally synthesizes routing policies after disambiguation and then verifies them. Our treatment of ambiguities is useful more generally when the intent of updates can be correctly synthesized by LLMs, but their integration is ambiguous and can lead to different global behaviors.