Comprehensive Survey on Techniques for Improving Cross-Lingual Alignment in Multilingual Language Models

Core Concepts

Cross-lingual alignment, the meaningful similarity of representations across languages in multilingual language models, is crucial for zero-shot cross-lingual transfer. This survey provides a comprehensive overview of techniques to improve cross-lingual alignment, including objectives using parallel data, contrastive learning, modified pre-training schemes, adapter tuning, and data augmentation.

Abstract

This survey provides a comprehensive overview of techniques to improve cross-lingual alignment in multilingual language models. The authors first define two main views of cross-lingual alignment - the "similarity-based" view and the "subspace-based" view. They then present a taxonomy of methods for improving alignment, categorized by their initialization (from existing model or from scratch) and data requirements (parallel data at sentence or word level, target task data, or other sources). The authors discuss the effectiveness of contrastive training for cross-lingual transfer, noting that pre-training alone does not determine performance. They also highlight that related languages tend to be more aligned within the models. The authors argue that "strong" alignment, as defined by the similarity-based view, may not be necessary for all tasks, and that effectively trading off language-neutral and language-specific information is key. The survey then discusses the emerging challenges posed by multilingual generative models, where simply maximizing cross-lingual alignment can lead to wrong-language generation. The authors call for future methods that can balance cross-lingual semantic information with language-specific factors, enabling fluent and relevant generation in multiple languages.

Stats

None

Quotes

None

Key Insights Distilled From

Understanding Cross-Lingual Alignment -- A Survey

by Kath... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06228.pdf

Deeper Inquiries

How can we effectively evaluate cross-lingual alignment in generative language models, beyond just classification tasks?

In evaluating cross-lingual alignment in generative language models, it is essential to go beyond traditional classification tasks and consider tasks that require generation, such as machine translation or text summarization. One effective approach is to use metrics that assess the quality of generated text in different languages. For instance, BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) are commonly used metrics for evaluating the quality of machine translation and text summarization outputs. These metrics can be adapted to evaluate the cross-lingual alignment of generative language models by comparing the generated text in different languages to reference translations or summaries. Another approach is to conduct human evaluations where bilingual or multilingual speakers assess the quality and fluency of the generated text in different languages. Human evaluations can provide valuable insights into how well the generative language model captures the nuances and idiosyncrasies of different languages, which is crucial for assessing cross-lingual alignment. Additionally, exploring tasks that require cross-lingual transfer of information, such as cross-lingual question answering or cross-lingual natural language understanding, can also be valuable for evaluating the effectiveness of cross-lingual alignment in generative language models. These tasks can help assess how well the model can transfer knowledge and information across languages, indicating the level of cross-lingual alignment achieved.

What are the potential downsides of overly emphasizing "strong" cross-lingual alignment, and how can we find the right balance between language-neutral and language-specific information?

Overly emphasizing "strong" cross-lingual alignment can lead to several potential downsides. One major downside is the risk of losing language-specific information that is crucial for tasks requiring language-specific nuances or cultural context. Strong alignment may prioritize similarity between representations of different languages to the extent that it diminishes the unique characteristics of each language, potentially impacting the model's performance on language-specific tasks. To find the right balance between language-neutral and language-specific information, it is essential to consider the task requirements and the intended use of the generative language model. One approach is to incorporate language-specific subspaces or features that capture language-specific nuances while maintaining cross-lingual alignment at a broader level. By identifying and preserving language-specific information in dedicated subspaces, the model can retain the richness and diversity of each language while still achieving effective cross-lingual alignment. Additionally, leveraging techniques such as fine-tuning with task-specific data or using adapters to modify the model for specific tasks can help strike a balance between language-neutral and language-specific information. These approaches allow for task-specific adjustments while maintaining the overall cross-lingual alignment of the model, ensuring optimal performance across a range of tasks and languages.

How can we extend cross-lingual alignment techniques to handle more diverse language pairs, beyond the typical English-centric setup?

To extend cross-lingual alignment techniques to handle more diverse language pairs beyond the typical English-centric setup, several strategies can be employed: Utilize Multilingual Data: Incorporate parallel data and resources from a wide range of languages to train the model on diverse language pairs. By including data from underrepresented languages, the model can learn to align representations across a more diverse set of languages. Adapt Alignment Objectives: Modify alignment objectives to account for the linguistic diversity and typological differences present in non-English languages. Tailoring alignment objectives to capture the unique characteristics of each language pair can improve the model's ability to align representations effectively. Fine-Tuning on Multiple Source Languages: Explore fine-tuning strategies that involve training the model on multiple annotated source languages. By fine-tuning on a diverse set of source languages, the model can learn to generalize better across different language pairs and adapt to the specific characteristics of each language. Data Augmentation and Synthetic Data: Generate synthetic parallel data or use data augmentation techniques to create more training examples for diverse language pairs. This can help improve the model's ability to align representations for languages with limited parallel data. Evaluate on Diverse Tasks: Evaluate the model's cross-lingual alignment performance on a variety of tasks beyond classification, such as machine translation, sentiment analysis, or named entity recognition. By assessing alignment across a range of tasks, the model's robustness and generalization capabilities can be tested across diverse language pairs.