Summary of Parameter-efficient Sparsity Crafting From Dense to Mixture-of-experts For Instruction Tuning on General Tasks, by Haoyuan Wu et al.
Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
by Haoyuan Wu, Haisheng Zheng, Zhuolun He, Bei Yu
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 This paper introduces parameter-efficient sparsity crafting (PESC), a novel method to expand the capacity of large language models (LLMs) during instruction tuning. PESC leverages the mixture-of-experts (MoE) architecture to craft dense models into sparse models, reducing computational costs and GPU memory requirements while maintaining the quality of approximation. The authors demonstrate the effectiveness of PESC by outperforming other sparse and dense models, including GPT-3.5, in general natural language processing tasks. This breakthrough has significant implications for the development of more capable and efficient LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make big language models better. It’s like teaching a super smart student how to do lots of things at once. The old way was not very good because it used too much computer power and memory. But this new method, called PESC, makes the model smaller while keeping its intelligence. This helps the model learn faster and be more efficient. The people who did the research tested it with a big language model and found that it worked really well. Now they want to share their code so others can use it too. |
Keywords
» Artificial intelligence » Gpt » Instruction tuning » Language model » Mixture of experts » Natural language processing » Parameter efficient
