Precisely annotating large-scale 3D datasets for point cloud segmentation is laborious. To alleviate the annotation burden, several semi-supervised 3D segmentation methods have been proposed in literature. However, two issues remain to be tackled: 1) The utilization of large language-vision models (LVM) in semi-supervised 3D semantic segmentation remains under-explored. 2) The unlabeled points with low-confidence predictions are directly discarded by existing methods. Taking these two issues into consideration, we propose a language-assisted semi-supervised 3D semantic segmentation method named LASS3D, which is built upon the commonly used MeanTeacher framework. In LASS3D, we use two off-the-shelf LVM to generate multi-level captions and leverage the images as the bridge to connect the text data and point clouds. Then, a semantic-aware adaptive fusion module is explored in the student branch, where the semantic information encoded in the embeddings of multi-level captions is injected into 3D features by adaptive fusion and then the semantic information in the text-enhanced 3D features is transferred to the teacher branch by knowledge distillation. In addition, a progressive exploitation strategy is explored for the unreliable points in the teacher branch, which can effectively exploit the information encapsulated in unreliable points via negative learning. Experimental results on both outdoor and indoor datasets demonstrate that LASS3D outperforms the comparative methods in most cases. We will release our code upon publication.