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

• URL: http://arxiv.org/abs/2512.09552v1
• Date: Wed, 10 Dec 2025 11:43:17 GMT
• Title: Systematic Framework of Application Methods for Large Language Models in Language Sciences
• Authors: Kun Sun, Rong Wang,
• Abstract summary: Large Language Models (LLMs) are transforming language sciences.<n>Their widespread deployment currently suffers from methodological fragmentation and a lack of systematic soundness.<n>This study proposes two comprehensive methodological frameworks designed to guide the strategic and responsible application of LLMs in language sciences.
• Score: 23.77480663886995
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 of each method, supported by empirical case studies. 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 system is fundamental for ensuring reproducibility, facilitating the critical evaluation of LLM mechanisms, and providing the structure necessary to move traditional linguistics from ad-hoc utility to verifiable, robust science.

Related papers

• Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms [0.0]
  This article presents the first systematic review of unsupervised and semi-supervised computational text-based ideal point estimation (CT-IPE) algorithms.<n>CT-IPE algorithms are widely used in political science, communication, computational social science, and computer science to estimate ideological preferences from parliamentary speeches, party manifestos, and social media.
  arXiv  Detail & Related papers  (2025-11-17T11:01:09Z)
• Context-Aware Hierarchical Taxonomy Generation for Scientific Papers via LLM-Guided Multi-Aspect Clustering [59.54662810933882]
  Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models, often lack coherence and granularity.<n>We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering.
  arXiv  Detail & Related papers  (2025-09-23T15:12:58Z)
• LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios [63.08653028889316]
  We propose a systematic taxonomy that decomposes agentic reasoning frameworks and analyze how these frameworks dominate framework-level reasoning.<n>Specifically, we propose an unified formal language to further classify agentic reasoning systems into single-agent methods, tool-based methods, and multi-agent methods.<n>We provide a comprehensive review of their key application scenarios in scientific discovery, healthcare, software engineering, social simulation, and economics.
  arXiv  Detail & Related papers  (2025-08-25T06:01:16Z)
• A Hybrid AI Methodology for Generating Ontologies of Research Topics from Scientific Paper Corpora [6.384357773998868]
  Sci-OG is a semi-auto-mated methodology for generating research topic.<n>This paper presents Sci-OG, a semi-auto-mated methodology for generating research topic.<n>We evaluate this approach against a range of alternative solutions using a dataset of 21,649 manually annotated semantic triples.
  arXiv  Detail & Related papers  (2025-08-06T08:48:14Z)
• A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems [93.8285345915925]
  Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making.<n>With the rapid advancement of large language models (LLMs), reasoning has emerged as a key capability that distinguishes advanced AI systems.<n>We categorize existing methods along two dimensions: (1) Regimes, which define the stage at which reasoning is achieved; and (2) Architectures, which determine the components involved in the reasoning process.
  arXiv  Detail & Related papers  (2025-04-12T01:27:49Z)
• Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey [64.08485471150486]
  This survey examines evaluation methods for large language model (LLM)-based agents in multi-turn conversational settings.<n>We systematically reviewed nearly 250 scholarly sources, capturing the state of the art from various venues of publication.
  arXiv  Detail & Related papers  (2025-03-28T14:08:40Z)
• A Survey on Mechanistic Interpretability for Multi-Modal Foundation Models [74.48084001058672]
  The rise of foundation models has transformed machine learning research.<n> multimodal foundation models (MMFMs) pose unique interpretability challenges beyond unimodal frameworks.<n>This survey explores two key aspects: (1) the adaptation of LLM interpretability methods to multimodal models and (2) understanding the mechanistic differences between unimodal language models and crossmodal systems.
  arXiv  Detail & Related papers  (2025-02-22T20:55:26Z)
• Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation [0.0]
  This study presents a framework for automated evaluation of dynamically evolving topic in scientific literature using Large Language Models (LLMs)<n>The proposed approach harnesses LLMs to measure key quality dimensions, such as coherence, repetitiveness, diversity, and topic-document alignment, without heavy reliance on expert annotators or narrow statistical metrics.
  arXiv  Detail & Related papers  (2025-02-11T08:23:56Z)
• Recommending Actionable Strategies: A Semantic Approach to Integrating Analytical Frameworks with Decision Heuristics [0.0]
  We present a novel approach for recommending actionable strategies by integrating strategic frameworks with decisions through semantic analysis.<n>Our methodology bridges this gap using advanced natural language processing (NLP), demonstrated through integrating frameworks like the 6C model with the Thirty-Six Stratagems.
  arXiv  Detail & Related papers  (2025-01-24T16:53:37Z)
• Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework [56.858564736806414]
  This paper proposes a novel framework leveraging large language models (LLMs) to identify interventions in scientific literature.<n>Our approach successfully identified 2,421 interventions from a corpus of 64,177 research articles in the speech-language pathology domain.
  arXiv  Detail & Related papers  (2024-08-20T16:42:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.