SELGJun 9, 2025

Towards a Small Language Model Lifecycle Framework

arXiv:2506.07695v1h-index: 10
Originality Synthesis-oriented
AI Analysis

This provides a foundational framework for researchers and practitioners working on SLMs, though it is incremental as it synthesizes existing knowledge rather than introducing new methods.

The study tackled the fragmented research on Small Language Models (SLMs) by proposing a modular lifecycle framework based on a survey of 36 works, aiming to unify development and maintenance practices.

Background: The growing demand for efficient and deployable language models has led to increased interest in Small Language Models (SLMs). However, existing research remains fragmented, lacking a unified lifecycle perspective. Objective: This study aims to define a comprehensive lifecycle framework for SLMs by synthesizing insights from academic literature and practitioner sources. Method: We conducted a comprehensive survey of 36 works, analyzing and categorizing lifecycle-relevant techniques. Results: We propose a modular lifecycle model structured into main, optional, and cross-cutting components. The model captures key interconnections across stages, supporting method reuse, co-adaptation, and lifecycle-awareness. Conclusion: Our framework provides a coherent foundation for developing and maintaining SLMs, bridging theory and practice, and guiding future research and tool development.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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