We introduce HYDRA, a simple yet effective multi-head encoder-only architecture for hierarchical text classification that treats each level in the hierarchy as a separate classification task with its own label space. State-of-the-art approaches rely on complex components like graph encoders, label semantics, and autoregressive decoders. We demonstrate that such complexity is often unnecessary. Through parameter sharing and level-specific parameterization, HYDRA enables flat models to incorporate hierarchical awareness without architectural complexity. Experiments on four benchmarks (NYT, RCV1-V2, BGC, and WOS) demonstrate that HYDRA always increases the performance over flat models and matches or exceeds the performance of complex state-of-the-art methods.

Language models can serve as a valuable tool for software developers to increase productivity. Large generative models can be used for code generation and code completion, while smaller encoder-only models are capable of performing code search tasks using natural language queries. These capabilities are heavily influenced by the quality and diversity of the available training data. Source code datasets used for training usually focus on the most popular languages and testing is mostly conducted on the same distributions, often overlooking low-resource programming languages. Motivated by the NLP generalization taxonomy proposed by Hupkes et.,al., we propose a new benchmark dataset called GenCodeSearchNet (GeCS) which builds upon existing natural language code search datasets to systemically evaluate the programming language understanding generalization capabilities of language models. As part of the full dataset, we introduce a new, manually curated subset StatCodeSearch that focuses on R, a popular but so far underrepresented programming language that is often used by researchers outside the field of computer science. For evaluation and comparison, we collect several baseline results using fine-tuned BERT-style models and GPT-style large language models in a zero-shot setting.