Multilingual Language Models for Zero-Shot Cross-Lingual Knowledge Transfer in Text Generation

Core Concepts

Multilingual pretrained language models can be finetuned on a task in one language and applied to make predictions in other languages, enabling zero-shot cross-lingual knowledge transfer. This work empirically studies the performance of different multilingual models, adaptation methods, and hyperparameter tuning for this setting, focusing on text generation tasks.

Abstract

The authors conduct an empirical study on zero-shot cross-lingual knowledge transfer in text generation using multilingual pretrained language models (mPLMs). They focus on encoder-decoder models, considering mT5, mBART, and NLLB-200 as the base models, and studying full finetuning as well as parameter-efficient finetuning with adapters. Key findings: The learning rate plays a crucial role, with lower learning rates helping to alleviate the problem of generating text in the wrong language. mBART with adapters performs similarly to mT5 of the same size, with mBART being better suited for tasks with long outputs and mT5 for tasks with short outputs. NLLB-200 performs well in summarization, especially for high-resource Latin-alphabet languages, but lags behind in question answering. Careful hyperparameter tuning, especially of the learning rate, is important to achieve good cross-lingual generation performance.

Stats

"The use of the language code in the decoder can help to alleviate the problem of generation in a wrong language." "Translation-based pretraining may provide good representations for cross-lingual transfer." "Larger learning rates lead to the model overfitting to the source English language and generating answers in English when applied in cross-lingual setting." "Reducing the learning rate helps to almost completely eliminate the problem of generating text in the wrong language, without hurting performance."

Quotes

"While inspecting predictions in Russian and French, we found that models achieving highest scores in both tasks generate fluent, meaningful and reasonable predictions in a lot of cases, but sometimes have issues with truthfulness or hallucinations." "Increasing LR leads first to increase in code switching and then to wrong language generation, while reducing LR leads to producing rudiments of pretraining in generation."

Key Insights Distilled From

Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation

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

https://arxiv.org/pdf/2310.09917.pdf

Deeper Inquiries

How can the insights from this study be applied to improve zero-shot cross-lingual generation in other domains beyond summarization and question answering

The insights from this study can be applied to improve zero-shot cross-lingual generation in other domains by considering the following strategies: Hyperparameter Tuning: Similar to the study's emphasis on tuning the learning rate to alleviate the problem of generation in the wrong language, researchers in other domains can conduct thorough hyperparameter searches to optimize model performance for specific tasks. Adaptation Methods: Experimenting with different adaptation methods, such as full finetuning and parameter-efficient finetuning with adapters, can help in improving zero-shot cross-lingual generation across various domains. Understanding which method works best for different tasks can enhance model performance. Model Selection: Exploring a variety of mPLMs with different architectural details and pretraining procedures can provide valuable insights into which models are more suitable for specific tasks. By comparing the performance of different models, researchers can identify the most effective model for zero-shot cross-lingual generation in different domains. Task-Specific Fine-tuning: Tailoring the fine-tuning process based on the specific requirements of the task in different domains can lead to better zero-shot cross-lingual generation results. Adapting the fine-tuning strategy to suit the characteristics of the data and the task can improve model performance. By applying these strategies and leveraging the findings from this study, researchers can enhance zero-shot cross-lingual generation in a wide range of domains beyond summarization and question answering.

What are the potential limitations of the proposed approaches, and how could they be addressed in future research

Some potential limitations of the proposed approaches in the study include: Generalizability: The study focused on summarization and question answering tasks, and the findings may not directly translate to other domains. Future research should validate the proposed approaches in diverse domains to ensure their effectiveness across different tasks. Data Availability: The success of zero-shot cross-lingual generation heavily relies on the availability of high-quality training data in multiple languages. Limited or biased data can impact the model's performance. Addressing data scarcity and ensuring data quality is crucial for improving model robustness. Language Complexity: The study primarily focused on languages with Latin and non-Latin scripts. Extending the research to include languages with more complex linguistic structures and characteristics can provide a more comprehensive understanding of zero-shot cross-lingual generation challenges and solutions. To address these limitations in future research, it is essential to conduct experiments across a wider range of domains, ensure diverse and representative training data, and consider the linguistic diversity of languages to enhance the applicability of the proposed approaches.

Given the differences in architectural details and pretraining procedures across mPLMs, how could these be leveraged to further enhance zero-shot cross-lingual generation capabilities

The differences in architectural details and pretraining procedures across mPLMs can be leveraged to further enhance zero-shot cross-lingual generation capabilities in the following ways: Architectural Adaptation: Researchers can explore modifying the architecture of mPLMs to better suit the requirements of zero-shot cross-lingual generation. Customizing the model architecture based on the specific characteristics of the task and languages involved can lead to improved performance. Transfer Learning Techniques: Leveraging transfer learning techniques that take into account the unique pretraining procedures of different mPLMs can enhance knowledge transfer across languages. Adapting transfer learning strategies to capitalize on the strengths of each model's pretraining can lead to more effective zero-shot cross-lingual generation. Multimodal Integration: Integrating multimodal capabilities into mPLMs can enable them to process and generate content across different modalities, such as text and images, in a cross-lingual context. This integration can enhance the model's understanding and generation of diverse content types in multiple languages. By strategically utilizing the architectural differences and pretraining procedures of mPLMs, researchers can develop more sophisticated models for zero-shot cross-lingual generation that are tailored to the specific requirements of different tasks and languages.