ARGUS: Retrieval-Augmented QA System for Government Services

The emergence of large language models (LLMs) has introduced new possibilities for government-oriented question-answering (QA) systems. Nonetheless, limitations in retrieval accuracy and response quality assessment remain pressing challenges. This study presents ARGUS (Answer Retrieval and Governance Understanding System), a fine-tuned LLM built on a domain-adapted framework that incorporates hybrid retrieval strategies using LlamaIndex. ARGUS improves factual consistency and contextual relevance in generated answers by incorporating both graph-based entity retrieval and associated text retrieval. A comprehensive evaluation protocol combining classical metrics and RAGAS indicators is employed to assess answer quality. The experimental results show that ARGUS achieved a ROUGE-1 score of 0.68 and a semantic relevance score of 0.81. To validate the effectiveness of individual system components, a chain-of-thought mechanism inspired by human reasoning was employed to enhance interpretability. Ablation results revealed improvements in ROUGE-1 to 68.5% and S-BERT to 74.9%, over 20 percentage points higher than the baseline. Additionally, the hybrid retrieval method outperformed pure vector (0.73) and pure graph-based (0.71) strategies, achieving an F1 score of 0.75. The main contributions of this study are twofold: first, it proposes a hybrid retrieval-augmented QA framework tailored for government scenarios; second, it demonstrates the system’s reliability and practicality in addressing complex government-related queries through the integration of human-aligned metrics and traditional evaluation methods. ARGUS offers a novel paradigm for providing trustworthy, intelligent government QA systems.