Summary of A Little Help Goes a Long Way: Efficient Llm Training by Leveraging Small Lms, By Ankit Singh Rawat et al.
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs
by Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, Rakesh Shivanna, Sashank J. Reddi, Aditya Krishna Menon, Rohan Anil, Sanjiv Kumar
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposed paradigm for large language model (LLM) development aims to improve efficiency and quality by leveraging small language models (SLMs). This is achieved through SLM-provided soft labels, which serve as additional training supervision, and the selection of a subset of valuable (“informative” and “hard”) training examples. The empirical results show reduced LLM training time compared to standard training, while maintaining overall quality. A statistical framework is developed to theoretically study the utility of SLMs in enabling efficient training of high-quality LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very powerful tools that help computers understand and generate human-like text. However, they require a lot of computational power and time to train. Researchers have been trying to find ways to make them more efficient without sacrificing their quality. This paper proposes an innovative approach by using smaller language models as “teachers” to guide the training process of larger language models. By providing soft labels (like hints) and selecting important examples, this method can reduce the training time while maintaining good results. |
Keywords
» Artificial intelligence » Large language model
