Systematic Framework of Application Methods for Large Language Models in Language Sciences

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Large Language Models (LLMs) are transforming language sciences. However, their widespread deployment currently suffers from methodological fragmentation and a lack of systematic soundness. This study proposes two comprehensive methodological frame works designed to guide the strategic and responsible application of LLMs inlanguage sciences. The first method-selection framework defines and systematizes three distinct, complementary approaches, each linked toa specific research goal: (1) prompt-based interaction with general-usemodels for exploratory analysis and hypothesis generation; (2) fine tuning of open-source models for confirmatory, theory-driven investigation and high-quality data generation; and (3) extraction of contextualized embeddings for further quantitative analysis and probing of model internal mechanisms. We detail the technical implementationand inherent trade-offs of each method, supported by empirical casestudies. Based on the method-selection framework, the second systematic framework proposed provides constructed configurations that guide the practical implementation of multi-stage research pipelines based on these approaches. We then conducted a series of empirical experiments to validate our proposed framework, employing retrospective analysis, prospective application, and an expert evaluation survey. By enforcing the strategic alignment of research questions with the appropriate LLM methodology, the frameworks enable a critical paradigm shift in language science research. We believe that this systemis fundamental for ensuring reproducibility, facilitating the criticalevaluation of LLM mechanisms, and providing the structure necessaryto move traditional linguistics from ad-hoc utility to verifiable, robust science.