Knowledge Distillation and Self-Distillation for Language Models

Dheiver Francisco Santos

doi:10.5281/zenodo.21610100

July 26, 2026Open Access

Knowledge Distillation and Self-Distillation for Language Models

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Key Points

This research aims to explore knowledge distillation and self-distillation to enhance language model efficiency without sacrificing performance.
Examined knowledge distillation techniques for compressing large language models.
Evaluated self-distillation methods to maintain task performance in these models.
Knowledge distillation effectively compresses models while retaining high performance metrics.
Self-distillation shows promise in further improving model efficiency and task performance.

Implication

Technical note discusses improvements in language models through knowledge and self-distillation techniques.

Abstract

A technical note covering knowledge distillation and self-distillation techniques used to compress and improve large language models while retaining task performance.

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Cite This Study

Dheiver Francisco Santos (2026) studied this question.

synapsesocial.com/papers/6a6851112845b684d16f06c4 https://doi.org/10.5281/zenodo.21610100

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Knowledge Distillation and Self-Distillation for Language Models

Key Points

Implication

Abstract

Discussion

Cite This Study

Knowledge Distillation and Self-Distillation for Language Models

Key Points

Implication

Abstract

Discussion

Cite This Study

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