Summary of A Survey on Symbolic Knowledge Distillation Of Large Language Models, by Kamal Acharya et al.
A Survey on Symbolic Knowledge Distillation of Large Language Models
by Kamal Acharya, Alvaro Velasquez, Houbing Herbert Song
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey paper explores the emerging area of symbolic knowledge distillation in Large Language Models (LLMs), such as Generative Pre-trained Transformer-3 (GPT-3) and Bidirectional Encoder Representations from Transformers (BERT). As LLMs grow in scale and complexity, effectively harnessing their extensive knowledge becomes crucial. The paper categorizes existing research based on methodologies and applications, focusing on how symbolic knowledge distillation can enhance the transparency and functionality of smaller AI models. It discusses core challenges, including maintaining knowledge depth, and explores various approaches and techniques developed in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This survey paper looks at a way to make Large Language Models (LLMs) more understandable and useful. LLMs are really good at processing language, but they’re hard to understand because their “knowledge” is hidden inside complex computer programs. This paper shows how to take that knowledge and turn it into something simpler and easier to use. It’s important for making AI systems more accessible and efficient. |
Keywords
* Artificial intelligence * Bert * Encoder * Gpt * Knowledge distillation * Transformer
