Summary of A Teacher Is Worth a Million Instructions, by Nikhil Kothari et al.
A Teacher Is Worth A Million Instructions
by Nikhil Kothari, Ravindra Nayak, Shreyas Shetty, Amey Patil, Nikesh Garera
First submitted to arxiv on: 27 Jun 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 A novel approach to training large language models (LLMs) is proposed, addressing the challenges of data quality and instruction tuning. The method utilizes knowledge from larger LLMs through a mixture-of-experts architecture, leveraging their ability to capture diverse data variations as effective teachers for smaller models. Additionally, a post-training domain alignment phase is introduced, employing domain-specific expert models to enhance domain-specific knowledge while preserving generalizability. Experimental results demonstrate the effectiveness of this approach, surpassing state-of-the-art language models with larger parameter sizes in tasks such as machine translation (MT-Bench) and text classification (AlpacaEval), achieving scores of up to 7.9 and 93.04%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very smart computers that can learn from lots of data. But training them is hard because we need good data and the right way to teach them. Some bigger models are even better teachers for smaller models, so this research uses those larger models as a kind of mentor to help train the smaller ones. It also has a special step called domain alignment that helps the model learn specific things about different areas like medicine or history. This approach works really well and beats other state-of-the-art language models on tasks like translating text and classifying it. |
Keywords
» Artificial intelligence » Alignment » Instruction tuning » Mixture of experts » Text classification » Translation
