## 6 Discussion  
The DeepSeek model evaluated in this study was not the widely known 671B parameter version, but rather a significantly smaller 7B variant. While this model still demonstrated competent performance, its smaller scale likely limited its capacity for nuanced reasoning and deeper contextual understanding, especially when compared to larger proprietary systems. The limited parameter count restricts the model's ability to capture complex relationships between classification components, which is crucial for UDC recommen  
Figure 6: Boxplot visualization of the Jaro–Winkler similarity for models evaluated on documents in Slovenian. Figure 7: Boxplot visualization of the adjusted hierarchical similarity for models evaluated on documents in Slovenian. dation. Nonetheless, it represents a meaningful step in testing practical alternatives to massive-scale models, especially where resources are constrained. Similarly, the o1-mini model exhibited weaker results when compared to GPT-4o. This discrepancy can largely be attributed to model size and architecture. GPT-4o likely benefits from extensive fine-tuning, multimodal capabilities, and a larger parameter count, giving it a distinct advantage in both linguistic fluency and classification accuracy. o1-mini's underperformance reflects the challenges faced by smaller models when tackling structured, multi-level classification tasks. Still, its inclusion is valuable in assessing what performance trade-offs are acceptable for lightweight deployments. To manage computational resources during experimentation, smaller reasoning-capable models were selected. However, reasoning models are inherently more complex and computationally expensive, and their smaller-scale implementations underperformed in hierarchical classification tasks such as UDC code generation. This result is unsurprising, given that complex reasoning typically emerges only in larger models with sufficient training depth. Nevertheless, these smaller reasoning models provide insight into the baseline capabilities of LLMs when tasked with structured taxonomic reasoning, even if they currently fall short of full automation. Across the board, open-weight models lagged behind proprietary alternatives on all evaluation metrics. Despite their accessibility and customization potential, current open-weight models still fall short in precision and generalization when compared to systems developed by large private research labs with substantial training data and computational infrastructure. This gap highlights the resource imbalance between community-driven projects and industrial-scale AI development. However, open-source ecosystems continue to evolve rapidly, and their transparency makes them attractive for research, reproducibility, and domain adaptation in settings like digital libraries. Nonetheless, promising improvements were observed in successive versions of open-weight model families, particularly within the Llama line. This indicates that open-source development is actively closing the performance gap, and newer iterations are already exhibiting better understanding and classification abilities than their predecessors. Llama 3.3 for instance, showed better alignment with UDC's hierarchical logic and fewer overpredicted code components. If this trend continues, future releases may become viable alternatives to proprietary models in institutional settings. A consistent trend was observed in relation to model size: Models in the 4-7B range generally performed worse than their 70B counterparts, and both were outperformed by proprietary models whose parameter sizes are undisclosed. This reinforces the importance of scale, both in terms of raw parameters and the depth of training and fine-tuning. Larger models appear more capable of understanding classification hierarchies and responding to subtle textual cues. While they are often computationally prohibitive to run locally, cloud-based deployment or model distillation could bridge this performance-accessibility gap in real-world applications.