Summary of Knowledge Distillation Using Frontier Open-source Llms: Generalizability and the Role Of Synthetic Data, by Anup Shirgaonkar et al.
Knowledge Distillation Using Frontier Open-source LLMs: Generalizability and the Role of Synthetic Data
by Anup Shirgaonkar, Nikhil Pandey, Nazmiye Ceren Abay, Tolga Aktas, Vijay Aski
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The abstract discusses large language models (LLMs) like Llama-3.1-Instruct-405B, which excel at text generation, question answering, and natural language understanding tasks. However, these models incur higher inference costs and latency compared to smaller LLMs. To address this, knowledge distillation is used to train smaller student models using outputs from larger teacher models, retaining comparable accuracy while reducing cost and latency. The study evaluates the effectiveness of distillation with different Llama-3.1 teacher-student pairs across various tasks and datasets, showing that synthetic data improves student model accuracy and enables internalization of teacher reasoning ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do amazing things like generate text and answer questions! But they can be slow and expensive to use. This paper looks at how we can make smaller versions of these models while keeping them accurate, so we can use them more quickly and cheaply. The researchers tested different ways of doing this using a big teacher model called Llama-3.1-Instruct-405B, and found that adding fake data helped the smaller student models learn to do things just as well. They also showed that these smaller models could even pick up on how the bigger model thinks about problems! |
Keywords
» Artificial intelligence » Distillation » Inference » Knowledge distillation » Language understanding » Llama » Question answering » Student model » Synthetic data » Teacher model » Text generation
