Knowledge Distillation and Dataset Distillation of Large Language Models: Emerging Trends, Challenges, and Future Directions (2504.14772v1)

Abstract: The exponential growth of 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.

Knowledge Distillation and Dataset Distillation of LLMs: Emerging Trends, Challenges, and Future Directions

The paper provides a thorough survey of knowledge distillation (KD) and dataset distillation (DD) methodologies tailored for LLMs. Both techniques aim to address the computational and data efficiency challenges posed by LLMs while retaining their advanced reasoning and linguistic capabilities.

Key Methodologies

Knowledge Distillation (KD): The paper explores various KD strategies, highlighting their applicability to LLMs. Traditional KD methods focus on transferring knowledge from a large, pre-trained teacher model to a smaller student model by aligning their outputs or intermediate representations. The paper emphasizes several innovations in KD for LLMs:

• Task-Specific Distillation: This involves adjusting the KD process to focus on specific linguistic or reasoning tasks. It includes rationale-based distillation, which captures logical reasoning steps, and multi-teacher frameworks, which amalgamate insights from several teacher models to convey a rich set of skills to the student model.
• Dynamic and Adaptive Approaches: These involve continuous adaptation where both teacher and student models co-evolve or utilize iterative protocols to improve distillation outcomes.
• Uncertainty and Bayesian KD: Techniques to understudy the uncertainty in the distillation process, allowing student models to maintain or improve performance robustness.

Dataset Distillation (DD): The paper explores the DD approach, which synthesizes smaller, high-impact datasets for efficient training:

• Optimization-Based Approaches: These involve creating a compact dataset that induces training trajectories in the student model similar to the original large dataset. Gradient matching and trajectory matching provide essential methodologies for effective DD.
• Generative Data Distillation: Methods using generative models to create synthetic data that maintain the diversity and richness of the original datasets. This is particularly beneficial in curtailing data redundancies while ensuring high informational content.

Integration of KD and DD

A significant portion of the paper focuses on the integration of KD and DD. Combining these approaches aims to enhance LLM efficiency further:

• Knowledge Transfer via Dataset Distillation: Efficiently synthesizes datasets that reflect the knowledge of teacher models, thereby guiding the student models to learn effectively with minimized data and computational resources.
• Prompt-Based Data Synthesis for KD: The paper addresses using strategic prompts in generative models to create datasets that better facilitate KD, allowing for a more effective and focused transfer of knowledge while addressing task-specific needs.

Practical Implications and Applications

The implications of KD and DD span multiple domains:

• In healthcare, applications range from clinical decision support to drug discovery, leveraging distillation to create domain-adapted models that perform efficiently in resource-constrained environments without sacrificing accuracy or functionality.
• In education, the deployment of distilled LLMs facilitates real-time interaction and assessment on limited hardware by reducing computational demands while maintaining instructional efficacy.
• Bioinformatics benefits through accelerated data analysis and improved predictive capabilities via efficient model adaptation and knowledge transference.

Challenges and Future Directions

While KD and DD hold promise, several challenges are highlighted:

• Preservation of Advanced Capabilities: Compressing models without losing emergent properties such as reasoning or semantic diversity is a major challenge. Future work must develop mechanisms that ensure distilled models retain these complex abilities.
• Scalability and Efficiency: As LLMs grow, the scalability of KD and DD techniques needs enhancement to reduce computational overhead effectively.
• Evaluation Frameworks: Developing robust evaluation standards that go beyond static accuracy metrics to encompass capabilities like reasoning and contextual adaptation will be critical.

In conclusion, the paper highlights KD and DD as pivotal strategies for advancing the sustainability and accessibility of LLMs. Through innovative methodologies and integrated approaches, these techniques provide a roadmap for efficient model compression and deployment across diverse domains. Future work must address the outlined challenges, ensuring that LLMs continue to evolve in a manner that balances efficiency with preservation of advanced functionalities.