Adversarial Robustness in Retrieval-Augmented Generation for Quranic Studies Across Open-Source LLM Scales and Architectural

Accurate and contextually faithful responses are critical when applying large language models (LLMs) to sensitive and domain-specific tasks, such as answering queries related to quranic studies. General-purpose LLMs often struggle with hallucinations, where generated responses deviate from authoritative sources, raising concerns about their reliability in religious contexts. This challenge highlights the need for systems that can integrate domain-specific knowledge while maintaining response accuracy, relevance, and faithfulness. In this study, we investigate 13 open-source LLMs categorized in

Research goal: How does adversarial robustness in retrieval-augmented generation (measured by accuracy drop under perturbed or misleading prompts) vary across different open-source LLMs (7B vs. 70B) when applied to domain-specific tasks like Quranic studies, and what architectural modifications (e.g., attention mechanisms) mitigate this?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.