In manufacturing, minimizing operational delays is crucial for efficiency and resilience. Therefore, efficiently handling contingencies is essential in human-robot teams working on assembly (i.e., collaborative assembly) applications. This paper introduces a novel approach to generating contingency handling procedures by leveraging recent advances in Large Language Models (LLMs). Our approach uses LLMs to update the required tasks in hierarchical task networks (HTNs) to handle contingencies. The results demonstrate that our approach can handle various contingencies in assembly applications and minimize the impact on the assembly completion time.