Interactive Contextual Recalibration Framework for Semantic Precision in Large Language Models

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Abstract

Semantic misalignment and contextual drift often undermine the reliability of generative systems in producing coherent and precise outputs. A novel framework, Interactive Contextual Recalibration, was developed to dynamically refine linguistic outputs through iterative feedback mechanisms embedded within the generation process. Designed to operate seamlessly across a range of tasks, the framework integrates semantic evaluation engines with adaptive inference modules to ensure outputs consistently align with intended objectives. Experiments revealed significant improvements in semantic precision and contextual coherence, particularly in noisy environments and tasks involving domain-specific complexities. The system’s modular design enables robust scalability, facilitating its application to both low-resource languages and extended sequence generation without significant performance degradation. Comparative benchmarks highlighted the framework's superiority in maintaining interpretive accuracy while addressing ambiguities more effectively than conventional approaches. Resource efficiency analyses confirmed minimal computational overhead, affirming its viability for real-world deployments. Notable success in domain-specific applications, such as technical and legal contexts, demonstrates its versatility and relevance to specialized linguistic challenges. Through scalable recalibration and robust performance across diverse conditions, the framework sets a new standard for adaptive systems, redefining the boundaries of contextual accuracy in text generation.

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