Self-Reflective Retrieval and Reasoning for Reliable Large Language Model Generation
Keywords:
retrieval-augmented generation, self-reflection, large language models, reliability, reasoning, governance, infrastructure, fairness, hallucination, socio-technical systemsAbstract
Large language models have become central to knowledge-intensive generation, yet their reliability remains constrained by static parametric memory, opaque reasoning pathways, and weak grounding in authoritative sources. Retrieval-augmented generation offers a partial remedy by connecting generation to external evidence, while reasoning strategies such as chain-of-thought and action-oriented prompting improve multi-step inference. However, neither retrieval nor reasoning alone guarantees faithful, fair, and governable outputs in high-stakes environments. This paper examines self-reflective retrieval and reasoning as a system-level architecture for reliable large language model generation. It argues that reliability emerges not from a single model capability but from the interaction of retrieval infrastructure, semantic fusion, reflective control loops, evaluation regimes, institutional governance, and deployment economics. The discussion analyzes structural trade-offs involving latency, accuracy, coverage, provenance, privacy, energy consumption, and accountability. It also considers how self-reflection can reduce hallucination and improve attribution while introducing new risks such as overcorrection, collusion with flawed sources, and runaway inference costs. Drawing on research in retrieval augmentation, reasoning, hallucination mitigation, foundation model governance, and machine learning operations, the paper proposes an integrated perspective that treats self-reflective retrieval and reasoning as a socio-technical capability rather than a purely algorithmic one. It concludes that dependable generation requires continuous monitoring, transparent data lineage, contestable decision pathways, and policy frameworks that align technical design with public interest.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
