Large Language Models, Encoder Architectures and Hybrid Approaches for Patent Classification
Automated patent classification is essential for organizing technological knowledge and constructing indicators of technological change, specialization, and leadership. To identify the relative strengths and weaknesses of popular state- of-the-art approaches to this problem, we perform a controlled comparison of patent-specific encoders and open-weight local LLMs for hierarchical multi-label Cooperative Patent Classification (CPC). We find that the best-performing encoder (task-adapted BERT-for-Patents) outperforms the best-performing LLM (fine-tuned Qwen3.5-9B), while requiring one to two orders of magnitude less energy for inference. The two model families show complementary capabilities, which we leverage through a hybrid pipeline that routes patents with the highest encoder uncertainty to the LLM, yielding significant gains on this subset. We also find that across models, classification errors are especially pronounced in CPC categories that are cross-cutting or semantically broad - such as Section Y - and that they have substantial consequences for technology mapping and country and assignee rankings. The analysis covers predictive and hierarchical performance, computational cost and energy consumption, external validation on EPO patents, and the propagation of classification errors into downstream technological indicators, with implications for automated patent classification procedures used by patent offices, technology analysts, and scientometric researchers.