With the emergence of ChatGPT, Transformer models have significantly advanced text classification and related tasks. Decoder-only models such as Llama exhibit strong performance and flexibility, yet they suffer from inefficiency on inference due to token-by-token generation, and their effectiveness in text classification tasks heavily depends on prompt quality. Moreover, their substantial GPU resource requirements often limit widespread adoption. Thus, the question of whether smaller language models are capable of effectively handling text classification tasks emerges as a topic of significant interest. However, the selection of appropriate models and methodologies remains largely underexplored. In this paper, we conduct a comprehensive evaluation of prompt engineering and supervised fine-tuning methods for transformer-based text classification. Specifically, we focus on practical industrial scenarios, including email classification, legal document categorization, and the classification of extremely long academic texts. We examine the strengths and limitations of smaller models, with particular attention to both their performance and their efficiency in Video Random-Access Memory (VRAM) utilization, thereby providing valuable insights for the local deployment and application of compact models in industrial settings.

Since the United Nations defined the Sustainable Development Goals, studies have shown that these goals are interlinked in different ways. The concept of SDG interlinkages refers to the complex network of interactions existing within and between the SDGs themselves. These interactions are referred to as synergies and trade-offs. Synergies represent positive interactions where the progress of one SDG contributes positively to the progress of another. On the other hand, trade-offs are negative interactions where the progress of one SDG has a negative impact on another. However, evaluating such interlinkages is a complex task, not only because of the multidimensional nature of SDGs, but also because it is highly exposed to personal interpretation bias and technical limitations. Recent studies are mainly based on expert judgements, literature reviews, sentiment or data analysis. To remedy these limitations we propose the use of Small Language Models in addition of an advanced Retrieval Augmented Generation to distinguish synergies and trade-offs between SDGs. In order to validate our results, we have drawn on the study carried out by the European Commission’s Joint Research Centre which provides a database of interlinkages labelled according to the presence of synergies or trade-offs.