Provenance-Aware Retrieval and Evaluator Consensus for Low-Resource Domain Adaptation of Large Language Models
Keywords:
Large Language Models, Domain Adaptation, Provenance-Aware Retrieval, Evaluator ConsensusAbstract
The adaptation of large language models to low-resource domains remains a formidable challenge in contemporary artificial intelligence research. Standard fine-tuning approaches often require extensive domain-specific corpora, which are inherently scarce in specialized fields such as rare disease diagnostics, indigenous language processing, and niche legal frameworks. Retrieval-augmented generation has emerged as a promising alternative; however, in low-resource settings, the quality of retrieved information is highly variable, and models are prone to hallucinating connections between disparate or unreliable source documents. This paper proposes a novel framework integrating provenance-aware retrieval with an evaluator consensus mechanism to address these limitations. By explicitly modeling the origin, credibility, and temporal relevance of retrieved documents, the provenance-aware module ensures that only highly reliable context is provided to the generation module. Concurrently, the evaluator consensus mechanism employs a multi-agent validation protocol, wherein independent evaluator agents assess the retrieved context and proposed responses, requiring a weighted consensus before final generation. This dual approach significantly mitigates hallucination and enhances factual accuracy in knowledge-starved domains. Extensive empirical analysis across three distinct low-resource datasets demonstrates that the proposed framework outperforms conventional adaptation methods in precision, recall, and factual consistency. The results indicate that strict provenance tracking combined with multi-agent consensus provides a robust, scalable solution for deploying large language models in specialized environments where data scarcity and reliability are critical concerns.
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