Systematic Framework of Application Methods for Large Language Models in Language Sciences
Kun Sun, Rong Wang

TL;DR
This paper introduces systematic frameworks for applying Large Language Models in language sciences, aiming to improve methodological consistency, reproducibility, and scientific rigor in research practices.
Contribution
It presents two comprehensive frameworks that guide the strategic selection and implementation of LLM methods tailored to specific research goals in language sciences.
Findings
Empirical validation through case studies and experiments.
Frameworks improve research reproducibility and methodological clarity.
Expert evaluation supports framework effectiveness.
Abstract
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 frameworks designed to guide the strategic and responsible application of LLMs in language sciences. The first method-selection framework defines and systematizes three distinct, complementary approaches, each linked to a specific research goal: (1) prompt-based interaction with general-use models 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 implementation and inherent trade-offs…
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Taxonomy
TopicsComputational and Text Analysis Methods · Machine Learning in Materials Science · Topic Modeling
