Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation 事件

Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation arXiv:2605.25835v1 Announce Type: cross Abstract: This paper examines the specialization of Small Language Models (SLMs) with up to 4 billion parameters for generating artifacts in domain-specific languages (DSL). Kubernetes manifests are chosen as the target domain. We propose the context-instrumental data distillation method: the source corpus is formed through synthetic generation an