Summary of Moe-ct: a Novel Approach For Large Language Models Training with Resistance to Catastrophic Forgetting, by Tianhao Li et al.
MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
by Tianhao Li, Shangjie Li, Binbin Xie, Deyi Xiong, Baosong Yang
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel MoE-CT architecture introduced in this paper addresses the disparity in large language model (LLM) performance between high-resource languages and low-resource languages. The approach separates the base model’s learning from multilingual expansion, freezing original LLM parameters to preserve proficiency while adding a MoE module for low-resource language augmentation. This design outperforms conventional continual training methods, with marked improvements in multilingual benchmarks without sacrificing original language performance. Additionally, the MoE-CT framework demonstrates enhanced resistance to forgetting and superior transfer learning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to make large language models work better for languages that don’t have as many examples to learn from. It does this by creating a special architecture that keeps the original model’s good performance while also improving its ability to understand other languages. This helps keep the model from forgetting what it learned in the first place, and makes it more useful for tasks like language translation. |
Keywords
» Artificial intelligence » Large language model » Transfer learning » Translation
