InstructBridge: An Instruction-Tuning Framework for Cross-Domain Semantic Reasoning
Keywords:
Instruction Tuning,, Semantic Reasoning,, Cross-Domain Adaptation,, Natural Language Processing,Abstract
The capacity of artificial intelligence systems to perform semantic reasoning across diverse domains remains a central challenge in contemporary natural language processing. While instruction-tuning has emerged as a powerful technique to align language models with human intent, existing frameworks frequently suffer from performance degradation when applied to out-of-distribution domains. To address this persistent gap, this paper introduces InstructBridge, a novel instruction-tuning framework explicitly designed to facilitate cross-domain semantic reasoning. InstructBridge leverages a meta-instruction generation paradigm combined with a unified latent space alignment mechanism to bridge the semantic disconnect between high-resource source domains and low-resource target domains. Through comprehensive empirical evaluation across multiple specialized disciplines including legal analysis, medical diagnostics, and financial forecasting, we demonstrate that InstructBridge significantly mitigates catastrophic forgetting and domain-shift degradation. The framework introduces a structural approach to prompt generalization, enabling models to abstract underlying reasoning patterns rather than memorizing domain-specific vocabulary. Our experimental results indicate substantial improvements over state-of-the-art baselines in zero-shot and few-shot cross-domain generalization tasks. Ultimately, InstructBridge offers a robust, scalable solution for developing versatile reasoning agents capable of maintaining high semantic fidelity across heterogeneous knowledge domains.
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Copyright (c) 2026 Emily Rutherford,, Michael Vance,, Noah Takeda
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