insight - Language Technology - # Cross-Lingual Transfer Methods

AdaMergeX: Adaptive Adapter Merging for Cross-Lingual Transfer with Large Language Models

Q: How does AdaMergeX address the limitations of fine-tuning on specific languages for cross-lingual transfer?

AdaMergeX addresses the limitations of fine-tuning on specific languages for cross-lingual transfer by decoupling task ability and language ability. Instead of treating them as separate entities, AdaMergeX acknowledges the interconnection between task ability and language ability. By introducing a reference task that can be easily accessed in both high-resource and low-resource languages, AdaMergeX obtains the divergence between the target language and source language. This approach allows for transferring task proficiency from a source language to a target language without relying solely on fine-tuning in specific languages. Additionally, by merging adapters based on adaptive adapter merging methods, AdaMergeX effectively combines task ability with language ability to achieve successful cross-lingual transfer.

Q: What are the implications of decoupling task ability from language ability in cross-lingual transfer methods?

Decoupling task ability from language ability in cross-lingual transfer methods has significant implications for enhancing model performance across different languages and tasks. By separating these two abilities, it becomes possible to focus on improving each aspect independently before merging them effectively. Decoupling allows researchers to fine-tune models specifically for tasks in one source language while considering the unique linguistic characteristics of another target language through adaptive adapter merging techniques. This approach ensures that models can leverage their proficiency in solving tasks while adapting seamlessly to various languages without being constrained by traditional fine-tuning approaches limited to specific languages.

Q: How can adaptive adapter merging be applied beyond cross-lingual transfer scenarios?

Adaptive adapter merging can be applied beyond cross-lingual transfer scenarios to enhance model performance and adaptability across various domains and applications. Multitask Learning: Adaptive adapter merging can be utilized in multitask learning settings where multiple tasks need to be performed simultaneously or sequentially using shared parameters but distinct adapters tailored for each individual task. Domain Adaptation: In domain adaptation scenarios, adaptive adapter merging can help models adjust their representations based on data from different domains or sources without extensive retraining. Transfer Learning: Adaptive adapter merging techniques can facilitate efficient knowledge transfer between related tasks or datasets by selectively combining adapters based on similarities or differences among them. Model Compression: For model compression purposes, adaptive adapter merging enables reducing computational complexity by consolidating information learned from different parts of a large model into more compact structures while maintaining performance. By applying adaptive adapter merging creatively across diverse contexts beyond just cross-lingual scenarios, researchers and practitioners can unlock new possibilities for optimizing model efficiency, flexibility, and generalization capabilities across varied machine learning applications.

Conceitos essenciais

The author proposes AdaMergeX, a method that merges task ability and language ability for effective cross-lingual transfer using adaptive adapter merging.

Resumo

AdaMergeX introduces a new approach to cross-lingual transfer by merging task and language abilities. The method outperforms existing techniques across various multilingual tasks. By utilizing adaptive adapter merging, AdaMergeX achieves significant improvements in performance compared to traditional methods.

The paper addresses the challenges of limited training data in specific languages by decoupling task and language abilities. It introduces a reference task to obtain language ability and merges it with task ability through adapter merging. The proposed structure-adaptive adapter merging method aligns with how adapters are integrated with language models.

Experimental results demonstrate the effectiveness of AdaMergeX on reasoning, natural language understanding, and natural language generation tasks across multiple languages. The method consistently outperforms other state-of-the-art methods, showcasing its robustness and generalizability.

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arxiv.org

Estatísticas

"Our empirical results demonstrate that our approach yields new and effective cross-lingual transfer, outperforming existing methods across all settings."
"AdaMergeX achieves 8.0% and 15.9% absolute improvement on XCOPA and XQuAD respectively with XLM-R."
"Compared to MAD-X, AdaMergeX achieves 31.1% relative improvement on average in all languages and all tasks with Llama2."

Citações

"Our proposed AdaMergeX effectively transfers the target task proficiency from the source language to the target language."
"AdaMergeX performs consistently well on different adapters."
"The adaptive merging method is crucial for adapter merging."

Principais Insights Extraídos De

AdaMergeX

by Yiran Zhao,W... às arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18913.pdf

Perguntas Mais Profundas

How does AdaMergeX address the limitations of fine-tuning on specific languages for cross-lingual transfer?

AdaMergeX addresses the limitations of fine-tuning on specific languages for cross-lingual transfer by decoupling task ability and language ability. Instead of treating them as separate entities, AdaMergeX acknowledges the interconnection between task ability and language ability. By introducing a reference task that can be easily accessed in both high-resource and low-resource languages, AdaMergeX obtains the divergence between the target language and source language. This approach allows for transferring task proficiency from a source language to a target language without relying solely on fine-tuning in specific languages. Additionally, by merging adapters based on adaptive adapter merging methods, AdaMergeX effectively combines task ability with language ability to achieve successful cross-lingual transfer.

What are the implications of decoupling task ability from language ability in cross-lingual transfer methods?

Decoupling task ability from language ability in cross-lingual transfer methods has significant implications for enhancing model performance across different languages and tasks. By separating these two abilities, it becomes possible to focus on improving each aspect independently before merging them effectively. Decoupling allows researchers to fine-tune models specifically for tasks in one source language while considering the unique linguistic characteristics of another target language through adaptive adapter merging techniques. This approach ensures that models can leverage their proficiency in solving tasks while adapting seamlessly to various languages without being constrained by traditional fine-tuning approaches limited to specific languages.

How can adaptive adapter merging be applied beyond cross-lingual transfer scenarios?

Adaptive adapter merging can be applied beyond cross-lingual transfer scenarios to enhance model performance and adaptability across various domains and applications.

Multitask Learning: Adaptive adapter merging can be utilized in multitask learning settings where multiple tasks need to be performed simultaneously or sequentially using shared parameters but distinct adapters tailored for each individual task.

Domain Adaptation: In domain adaptation scenarios, adaptive adapter merging can help models adjust their representations based on data from different domains or sources without extensive retraining.

Transfer Learning: Adaptive adapter merging techniques can facilitate efficient knowledge transfer between related tasks or datasets by selectively combining adapters based on similarities or differences among them.

Model Compression: For model compression purposes, adaptive adapter merging enables reducing computational complexity by consolidating information learned from different parts of a large model into more compact structures while maintaining performance.

By applying adaptive adapter merging creatively across diverse contexts beyond just cross-lingual scenarios, researchers and practitioners can unlock new possibilities for optimizing model efficiency, flexibility, and generalization capabilities across varied machine learning applications.