Comprehensive Analysis of Multilingual Large Language Models: Corpora, Alignment, and Bias

Core Concepts

This survey provides a comprehensive analysis of Multilingual Large Language Models (MLLMs), delving into critical issues surrounding their training corpora, multilingual representation alignment, and inherent biases.

Abstract

This survey presents an overview of the evolution of Multilingual Large Language Models (MLLMs), tracing their development from monolingual Large Language Models (LLMs). It highlights the key techniques that contribute to the success of MLLMs, including transformer architecture, pre-training techniques, and reinforcement learning with human feedback. The survey then explores the multilingual capacities of MLLMs, discussing the challenges they face due to language imbalance in training corpora and the potential for cross-lingual transfer learning. It provides an in-depth analysis of the widely utilized multilingual corpora and datasets for training and fine-tuning MLLMs, offering insights into their language distribution, data sources, and language coverage. Next, the survey delves into the topic of multilingual representation alignment, categorizing the existing approaches into static, contextual, and combined methods. It examines the factors that affect the performance of these alignment techniques, such as initial alignment solution, language typological distance, and pre-training data and settings of MLLMs. Finally, the survey discusses the issue of bias in MLLMs, addressing questions about the types of bias present, the available debiasing techniques, and the impact of bias removal on model performance. It also summarizes the existing bias evaluation datasets for MLLMs. Throughout the survey, the author aims to facilitate a deeper understanding of MLLMs and their potential in various domains, while also highlighting the critical challenges and promising research directions in this field.

Stats

MLLMs are trained on corpora where English is the predominant language, accounting for over 90% in some cases. The Indo-European language family holds a prominent position in MLLM training corpora, both in terms of quantity and language diversity. The Sino-Tibetan language family, mainly consisting of the Chinese language, also has a significant presence in the training corpora. Other language families, such as Niger-Congo and Trans-New Guinea, are notably absent from the top 20 languages in the training data.

Quotes

"MLLMs broaden language coverage beyond LLMs, yet English remains dominant in their training corpora." "Further work should consider a more comprehensive inclusion of language families and prioritize language diversity within the same language family when training MLLMs." "The performance of MLLMs on multilingual alignment varies across languages, with better performance observed for English and its closely related languages."

Key Insights Distilled From

A Survey on Multilingual Large Language Models

by Yuemei Xu,Li... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00929.pdf

Deeper Inquiries

How can we effectively address the "curse of multilinguality" phenomenon in MLLMs to improve their performance on low-resource languages?

To address the "curse of multilinguality" phenomenon in Multilingual Large Language Models (MLLMs) and improve their performance on low-resource languages, several strategies can be implemented: Language Family Prioritization: By focusing on training MLLMs on languages within the same language family, we can leverage linguistic similarities to enhance cross-lingual transfer learning capabilities. This approach has shown to improve performance on low-resource languages as seen in models like AfriBERTa, which was pre-trained on African languages belonging to the Bantu branch of the Niger-Congo language family. Data Sampling Techniques: Implementing data sampling techniques like exponential weighted smoothing can help prevent under-representation of low-resource languages in the training data. This can balance the language distribution and improve the model's performance on these languages. Fine-tuning on Low-Resource Languages: Fine-tuning existing MLLMs to suit the linguistic features of low-resource languages can also enhance their performance. This approach requires specific-task annotated training data but can lead to improved results for these languages. Pre-training on Low-Resource Languages: Pre-training monolingual LLMs on low-resource languages without requiring extensive annotated data has shown to be effective in enhancing performance on these languages. Models like RoBERTa pre-trained for Czech, Polish, and Czech-Polish bilingual models have demonstrated superior performance on low-resource languages. By implementing these strategies, MLLMs can overcome the challenges posed by the "curse of multilinguality" and improve their performance on low-resource languages.

What are the potential biases introduced by the skewed language distribution in MLLM training corpora, and how can we mitigate these biases?

The skewed language distribution in Multilingual Large Language Model (MLLM) training corpora can introduce several biases, including: Language Imbalance Bias: Languages with larger proportions in the training data may receive more focus and better representation, leading to performance disparities across languages. This can result in biased outputs favoring high-resource languages over low-resource languages. Cultural Bias: The over-representation of certain languages can introduce cultural biases in the model's understanding and generation of text, impacting the accuracy and fairness of the model's outputs across different languages and cultures. Performance Bias: Skewed language distribution can lead to performance biases, where the model performs better on languages with larger training data and underperforms on languages with limited representation. To mitigate these biases, the following strategies can be employed: Balanced Language Distribution: Ensuring a more balanced language distribution in the training data can help reduce biases and improve the model's performance across all languages. Bias Evaluation Metrics: Implementing bias evaluation metrics specific to different languages and cultures can help identify and quantify biases in the model's outputs, enabling targeted mitigation strategies. De-biasing Techniques: Utilizing de-biasing techniques such as adversarial training, data augmentation, and bias-aware fine-tuning can help mitigate biases introduced by skewed language distribution in MLLM training corpora. By addressing these potential biases and implementing mitigation strategies, MLLMs can enhance their fairness, accuracy, and performance across diverse languages and cultures.

How can we leverage the linguistic similarities within language families to enhance the cross-lingual transfer learning capabilities of MLLMs?

Leveraging the linguistic similarities within language families can significantly enhance the cross-lingual transfer learning capabilities of Multilingual Large Language Models (MLLMs) in the following ways: Improved Transfer Learning: By training MLLMs on languages within the same language family, models can leverage shared linguistic features and structures to improve cross-lingual transfer learning. This approach enhances the model's ability to generalize across languages within the same family. Enhanced Performance on Low-Resource Languages: Languages within the same language family often share common vocabulary and syntactic patterns. By focusing on these similarities, MLLMs can achieve better performance on low-resource languages within the same language family, even with limited training data. Reduced Semantic Drift: Linguistic similarities within language families help reduce semantic drift, where the meaning of words or phrases changes across languages. MLLMs trained on languages with shared linguistic roots are better equipped to handle semantic consistency and improve translation accuracy. Domain Adaptation: Leveraging linguistic similarities within language families can facilitate domain adaptation for MLLMs. Models trained on languages with similar linguistic structures can more effectively transfer knowledge and adapt to new domains or tasks. By capitalizing on the linguistic similarities within language families, MLLMs can enhance their cross-lingual transfer learning capabilities, improve performance on low-resource languages, and achieve more accurate and consistent results across diverse linguistic contexts.