Project Introduction: Awesome Knowledge Distillation of Large Language Models
Overview
The Awesome Knowledge Distillation of Large Language Models project offers a comprehensive collection of research papers on the knowledge distillation process of large language models (LLMs). This initiative seeks to advance smaller models and make them more efficient by transferring complex capabilities from larger, proprietary models like GPT-4 to open-source counterparts such as LLaMA and Mistral. Knowledge distillation helps compress these models and fosters self-improvement by using the models themselves as teachers.
Knowledge Distillation and Data Augmentation
In this project, knowledge distillation (KD) is seen as a crucial bridge to bolster the performance of large language models. The associated survey explores how data augmentation (DA) works hand-in-hand with KD, creating a powerful paradigm shift. Through DA, the project generates training data rich in context and specific skills, enabling open-source models to mirror the adeptness and ethical insights of proprietary models.
Taxonomy and Structure
Knowledge distillation is analyzed through three essential pillars:
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Algorithms: This covers the technical methods used in knowledge distillation, from knowledge elicitation to distillation algorithms. It comprises steps like "Knowledge Elicitation" from teacher models and "Distillation Algorithms" for infusing insights into student models.
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Skill Distillation: The project examines the enhancement of cognitive abilities in LLMs, such as context awareness, ethical alignment, and multi-modality skills, making smaller models suitable for a broader array of tasks.
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Verticalization Distillation: The project delves into the practical applications of KD across various fields like law, healthcare, finance, science, and more, showing the versatility of distilled models in specialized areas.
Role of Knowledge Distillation in LLMs
In the current era dominated by large language models, knowledge distillation serves several pivotal roles:
- Advancing Smaller Models: It enables the transfer of advanced functionalities from large, proprietary models to smaller, open-source ones, facilitating public research and development.
- Compression: By compressing open-source models, KD makes them more practical and efficient, enabling widespread use across industries.
- Self-Improvement: KD allows open-source models to refine their abilities using their existing knowledge, continuously enhancing their capabilities and competitiveness.
News and Updates
This project is dynamic, with regular updates to include the latest research papers. Key milestones include the release of a survey paper titled "A Survey on Knowledge Distillation of Large Language Models," which has generated interest in the academic community.
How to Contribute
The project welcomes contributions from the wider research community. Interested individuals can open an issue or a pull request, or contact the project's contributors via email to suggest new research papers or improvements.
Conclusion
The Awesome Knowledge Distillation of LLMs project is an essential resource for anyone interested in the field. By offering insights into the mechanisms and applications of knowledge distillation, it paves the way for advances in language model research and real-world applications. For a deeper understanding, individuals are encouraged to explore the collection of papers and contribute to the ongoing development of this initiative.