Summary of Adamergex: Cross-lingual Transfer with Large Language Models Via Adaptive Adapter Merging, by Yiran Zhao et al.
AdaMergeX: Cross-Lingual Transfer with Large Language Models via Adaptive Adapter Merging
by Yiran Zhao, Wenxuan Zhang, Huiming Wang, Kenji Kawaguchi, Lidong Bing
First submitted to arxiv on: 29 Feb 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 proposed method in this paper addresses the limitations of traditional cross-lingual transfer techniques by introducing an adaptive adapter merging mechanism. This approach acknowledges the mutual reliance between task ability and language ability, and seeks to decouple them by fine-tuning on a reference task in both languages. The resulting target adapters are shown to outperform existing methods across all settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to improve cross-lingual transfer learning. Currently, this technique requires training models on specific tasks for each language, which can be time-consuming and data-hungry. Researchers have tried to find shortcuts by fine-tuning models in one language and applying them to another. However, these approaches often don’t fully separate the skills needed for different tasks from the languages themselves. The authors of this paper propose a new method that gets around this problem by using a special “reference” task to merge information from multiple languages and tasks. |
Keywords
» Artificial intelligence » Fine tuning » Transfer learning
