Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions

Authors

Luyang Fang
Xiaowei Yu
Jiazhang Cai
Yongkai Chen
Shushan Wu
Zhengliang Liu
Zhenyuan Yang
Haoran Lu
Xilin Gong
Yufang Liu
Terry Ma
Wei Ruan
Ali Abbasi
Jing Zhang
Tao Wang
Ehsan Latif
Wei Liu
Wei Zhang
Soheil Kolouri
Xiaoming Zhai
Dajiang Zhu
Wenxuan Zhong
Tianming Liu
Ping Ma

Language

English

Publication Date

1-1-2026

Journal

Artificial Intelligence Review

DOI

10.1007/s10462-025-11423-3

PMID

41278248

PMCID

PMC12634706

PubMedCentral® Posted Date

11-20-2025

PubMedCentral® Full Text Version

Post-print

Abstract

The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey provides a comprehensive analysis of two complementary paradigms: Knowledge Distillation (KD) and Dataset Distillation (DD), both aimed at compressing LLMs while preserving their advanced reasoning capabilities and linguistic diversity. We first examine key methodologies in KD, such as task-specific alignment, rationale-based training, and multi-teacher frameworks, alongside DD techniques that synthesize compact, high-impact datasets through optimization-based gradient matching, latent space regularization, and generative synthesis. Building on these foundations, we explore how integrating KD and DD can produce more effective and scalable compression strategies. Together, these approaches address persistent challenges in model scalability, architectural heterogeneity, and the preservation of emergent LLM abilities. We further highlight applications across domains such as healthcare and education, where distillation enables efficient deployment without sacrificing performance. Despite substantial progress, open challenges remain in preserving emergent reasoning and linguistic diversity, enabling efficient adaptation to continually evolving teacher models and datasets, and establishing comprehensive evaluation protocols. By synthesizing methodological innovations, theoretical foundations, and practical insights, our survey charts a path toward sustainable, resource-efficient LLMs through the tighter integration of KD and DD principles.

Keywords

Large language models, Knowledge distillation, Dataset distillation, Efficiency, Model compression, Survey

Published Open-Access

yes

Recommended Citation

Fang, Luyang; Yu, Xiaowei; Cai, Jiazhang; et al., "Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions" (2026). Faculty, Staff and Student Publications. 6736.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6736