Can retrieval-augmented 3B models achieve comparable robustness to distractor contexts in multi-hop QA tasks a

Abstract

Abstract: 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 Question

Can retrieval-augmented 3B models achieve comparable robustness to distractor contexts in multi-hop QA tasks as larger models while maintaining inference efficiency metrics like latency and throughput?

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Quality Dimensions

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