Large Language Models excel at NLP tasks but remain prone to hallucinations, limiting trust in real-world applications. We present HalluGuard, a 4B-parameter Small Reasoning Model (SRM) designed as a guardrail for Retrieval-Augmented Generation (RAG) pipelines, which classify document-claim pairs as grounded or hallucinated in closed-book, document-grounded settings and produces evidence-grounded justifications. Our approach combines (i) a domain-agnostic synthetic dataset derived from FineWeb and refined through multi-stage curation and data reformation, (ii) synthetic grounded and hallucinated claims, and (iii) preference-based fine-tuning with Odds Ratio Preference Optimization (ORPO) to distill large-model reasoning into a smaller backbone. On the RAGTruth subset of the LLM-AggreFact benchmark, HalluGuard achieves 84.4% balanced accuracy (BAcc), surpassing specialized models, MiniCheck (7B; 84.0%) and Granite Guardian 3.3 (8B; 82.2%) while using roughly half their parameters. Across the benchmark, it reaches 77.1% BAcc, surpassing larger general-purpose LLMs such as GPT-4o (75.9%). HalluGuard and datasets will be released upon acceptance.

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.