Summary of Layer Swapping For Zero-shot Cross-lingual Transfer in Large Language Models, by Lucas Bandarkar et al.
Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models
by Lucas Bandarkar, Benjamin Muller, Pritish Yuvraj, Rui Hou, Nayan Singhal, Hongjiang Lv, Bing Liu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Model merging, a technique that combines different models with the same architecture without further training, is crucial for leveraging Large Language Models (LLMs) in non-English languages. This paper presents a novel methodology for fine-tuning LLMs on target tasks in underserved languages, where task-specific data is scarce. By composing language and math capabilities, the proposed approach facilitates cross-lingual transfer without relying on in-language mathematical data. The method involves fine-tuning separate “experts” on English math instruction data and generic instruction data in the target language, then replacing top and bottom transformer layers with those from the language expert. This simple yet effective technique yields improved performance on the MGSM math benchmark by 10% across four major languages. The merged models outperform individual experts and other merging methods, demonstrating the potential for re-composing LLMs to create modular solutions and transfer reasoning capabilities across languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model mergers combine different models with the same architecture without further training. This paper solves a problem in language learning by combining language and math knowledge. It’s like using a special tool that can translate math problems from one language to another, making it easier to learn math in different languages. The researchers created two “experts” – one learned about math in English and the other learned about general things in the target language. They then mixed the experts together to create a new model that’s better at solving math problems in the target language. This new model works well across four major languages, making it easier for people to learn math in different countries. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
