Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models (2410.01335v2)

Abstract: Model merging, such as model souping, is the practice of combining different models with the same architecture together without further training. In this work, we present a model merging methodology that addresses the difficulty of fine-tuning LLMs for target tasks in non-English languages, where task-specific data is often unavailable. We focus on mathematical reasoning and without in-language math data, facilitate cross-lingual transfer by composing language and math capabilities. Starting from the same pretrained model, we fine-tune separate "experts" on math instruction data in English and on generic instruction data in the target language. We then replace the top and bottom transformer layers of the math expert directly with layers from the language expert, which consequently enhances math performance in the target language. The resulting merged models outperform the individual experts and other merging methods on the math benchmark, MGSM, by 10% across four major languages where math instruction data is scarce. In addition, this layer swapping is simple, inexpensive, and intuitive, as it is based on an interpretative analysis of the most important parameter changes during the fine-tuning of each expert. The ability to successfully re-compose LLMs for cross-lingual transfer in this manner opens up future possibilities to combine model expertise, create modular solutions, and transfer reasoning capabilities across languages all post hoc.

• The paper introduces a novel layer swapping technique that merges task-specific and language-specific experts from a single pre-trained model.
• It demonstrates a 10% performance boost on the MGSM benchmark across low-resource languages by strategically swapping transformer layers.
• The method presents an efficient blueprint for scalable multilingual AI, reducing retraining costs while maintaining high accuracy.

Insightful Overview of "Layer Swapping for Zero-Shot Cross-Lingual Transfer in LLMs"

This paper introduces an innovative approach to model merging specifically designed for enhancing the cross-lingual capabilities of LLMs. Centered around the concept termed as "Layer Swapping," the paper delineates a methodology that improves task-specific performance in non-English languages by effectively combining task-oriented and language-oriented model "experts."

Methodological Insights

The authors propose a solution to the lack of high-quality task-specific data in numerous non-English languages, particularly in domains like mathematical reasoning. Their method involves fine-tuning two distinct experts derived from a single pre-trained model: one expert is refined on English math instruction data, and the other on generic instruction data in the target language. By swapping the top and bottom transformer layers from the language expert into the math expert, the merged model exhibits improved task performance in the target language. This strategic selection is informed by an analysis revealing that mathematical reasoning capabilities predominantly concentrate in the middle layers of the model, whereas language representation is more pronounced in the initial and concluding layers.

Empirical Findings

The experimental results are compelling, demonstrating a significant 10\% performance improvement on the MGSM benchmark across Swahili, Telugu, Bengali, and Japanese, languages characterized by scarce math instruction data. This clear outperforming of both the individual experts and existing model merging methods, such as model souping, underscores the efficacy of layer swapping. The approach is noted for being both financially economical and conceptually straightforward, requiring only parameter-level augmentations which are computationally trivial.

Implications for LLMs and Future Work

The proposed layer swapping method transcends mere empirical success, suggesting broader theoretical and practical implications. It highlights latent structural patterns within LLMs and opens avenues for more refined, modular approaches to LLM specialization via post hoc model composition. This method, in essence, decouples linguistic and reasoning enhancements, offering a blueprint for scalable customizations across diverse languages.

Practically, this methodology can significantly reduce the cost and complexity associated with developing multilingual AI systems, especially for lower-resource languages. It indicates a pathway for AI systems where expertise in one language can be efficiently redirected to another, merely through strategic model reconfiguration without the need for extensive retraining.

Concluding Thoughts

The research establishes a promising precedent in AI's capacity for cross-lingual adaptability. While preliminary results are impressive, further exploration into diverse architectures, varied language tasks, and different-size models would extend and potentially refine the utility of layer swapping. Additionally, the integration with parameter-efficient approaches like LoRA and further investigation into its application during pretraining stages could optimize the method's versatility and efficiency. Ultimately, this paper not only offers a novel technical solution but also posits a meaningful step towards democratizing access to sophisticated AI across linguistic barriers.