Key Ingredients for Effective Zero-Shot Cross-Lingual Knowledge Transfer in Generative Tasks

核心概念

Careful learning rate tuning and intermediate tuning are key ingredients for achieving high-performing zero-shot cross-lingual transfer in generation, allowing simple full finetuning to closely approach or reach the performance of the computationally expensive data translation approach.

摘要

The paper investigates how to achieve high-performing zero-shot cross-lingual transfer in generation tasks. The key findings are:

Careful learning rate tuning is crucial - reducing the learning rate helps to almost completely eliminate the problem of generating text in the wrong language, which is a common issue reported in prior work.

Intermediate tuning on a language modeling-like task is often beneficial, especially for mBART, and helps to improve performance in the majority of cases.

With careful learning rate tuning and intermediate tuning, simple full finetuning is a very strong baseline that reaches or approaches the performance of the data translation approach, which is usually considered an upper baseline for zero-shot cross-lingual transfer in generation.

More advanced adaptation methods like freezing the decoder and embeddings, using multiple source languages, or parameter-efficient methods like prompt tuning, bring only modest improvements over the strong full finetuning baseline.

mBART and mT5 of similar sizes perform comparably. NLLB-200, a translation-focused model, performs well in summarization for high-resource Latin-alphabet languages, but lags behind in question answering.

The final zero-shot performance can match or exceed the performance of the data translation approach, which is computationally expensive.

統計資料

"Reducing the learning rate helps to almost completely eliminate the problem of generating text in the wrong language."
"Intermediate tuning on a language modeling-like task is often beneficial, especially for mBART, and helps to improve performance in the majority of cases."
"With careful learning rate tuning and intermediate tuning, simple full finetuning is a very strong baseline that reaches or approaches the performance of the data translation approach."

引述

"Careful learning rate tuning and intermediate tuning are key ingredients for achieving high-performing zero-shot cross-lingual transfer in generation, allowing simple full finetuning to closely approach or reach the performance of the computationally expensive data translation approach."
"More advanced adaptation methods like freezing the decoder and embeddings, using multiple source languages, or parameter-efficient methods like prompt tuning, bring only modest improvements over the strong full finetuning baseline."

從以下內容提煉的關鍵洞見

Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks

by Nadezhda Chi... 於 arxiv.org 04-23-2024

https://arxiv.org/pdf/2402.12279.pdf

深入探究

What other pretraining objectives or architectural choices could be explored to further improve zero-shot cross-lingual generation performance?

In order to enhance zero-shot cross-lingual generation performance, exploring different pretraining objectives and architectural choices could be beneficial. One approach could be to incorporate more diverse and challenging tasks during pretraining, such as document-level or conversational tasks, to improve the model's ability to generate coherent and contextually relevant responses in different languages. Additionally, incorporating language-specific pretraining objectives or fine-tuning strategies could help the model better capture language nuances and improve cross-lingual transfer performance. Architecturally, exploring more sophisticated ways to incorporate language-specific information, such as leveraging language embeddings or incorporating language-specific attention mechanisms, could also lead to performance improvements in zero-shot cross-lingual generation tasks.

How would the findings change if the study considered a wider range of low-resource languages or more diverse tasks beyond summarization and question answering?

If the study considered a wider range of low-resource languages or more diverse tasks beyond summarization and question answering, the findings may vary in several ways. Firstly, the performance of the models in zero-shot cross-lingual generation tasks could be impacted by the linguistic diversity and complexity of the languages included. Low-resource languages may pose additional challenges in terms of data availability and linguistic characteristics, potentially affecting the model's ability to generalize across languages. Additionally, exploring a broader range of tasks beyond summarization and question answering could provide insights into the generalizability of the models across different types of generative tasks, shedding light on the versatility and limitations of zero-shot cross-lingual transfer in diverse linguistic contexts.

Could the insights from this work be applied to improve few-shot cross-lingual transfer in generation as well?

The insights from this work on zero-shot cross-lingual transfer in generation could indeed be applied to improve few-shot cross-lingual transfer as well. By understanding the impact of hyperparameter tuning, adaptation methods, and model choices on zero-shot performance, researchers can leverage similar strategies to enhance few-shot cross-lingual transfer capabilities. Fine-tuning learning rates, incorporating intermediate tuning, and exploring different adaptation methods could help improve the model's ability to transfer knowledge across languages even with limited training data. Additionally, the findings on the effectiveness of different models and adaptation techniques can guide the development of more efficient and accurate few-shot cross-lingual generation models, enabling better performance with minimal labeled examples in the target language.