This paper presents the Annif system for topic indexing using large-scale language models (LLMs) in SemEval-2025 Task 5 (LLMs4Subjects). This task required generating topic predictions using the Globally Neural Network (GND) topic vocabulary for bibliographic records in the bilingual TIBKAT database. The Annif system combines existing natural language processing and machine learning techniques implemented in the Annif toolkit with an innovative LLM-based method for translation and synthetic data generation, as well as prediction merging of Japanese models. In quantitative evaluations, it ranked first in all subject categories, second in the tib-core-subject category, and fourth in qualitative evaluations. These results demonstrate the potential of combining the existing XMTC algorithm with modern LLM techniques to improve the accuracy and efficiency of topic indexing in multilingual environments.