Sign In

Multilingual Large Language Models: A Comprehensive Survey of Resources, Taxonomy, and Emerging Frontiers

Core Concepts
This paper presents a comprehensive survey of the recent progress and emerging trends in multilingual large language models (MLLMs), offering a unified perspective through a novel taxonomy based on alignment strategies.
The paper provides a thorough review of the advancements in multilingual large language models (MLLMs). It introduces a novel taxonomy that categorizes MLLMs into two main alignment strategies: parameter-tuning alignment and parameter-frozen alignment. Parameter-Tuning Alignment: Pretraining Alignment: Approaches that tune model parameters during the pretraining stage, including from-scratch pretraining and continual pretraining. Supervised Fine-Tuning (SFT) Alignment: Methods that leverage multilingual task data with instruction format to fine-tune model parameters. Reinforcement Learning from Human Feedback (RLHF) Alignment: Techniques that integrate multilingual RLHF data to train more effective reward models. Downstream Finetuning Alignment: Strategies that fine-tune model parameters on downstream tasks, including full-parameter and parameter-efficient approaches. Parameter-Frozen Alignment: Direct Prompting: Directly outputting requests without additional instructions for implicit alignment. Code-Switching Prompting: Integrating multilingual words into a single-language utterance to elicit alignment. Translation Alignment Prompting: Translating the query into other languages for better cross-lingual alignment. Retrieval Augmented Alignment: Incorporating external retrieval to inject more knowledge during prompting. The paper also highlights several emerging frontiers and challenges in the MLLM field, including hallucination, knowledge editing, safety, fairness, language extension, and multi-modality extension.
There are over 7,000 languages in the world, and the success of large language models should consider serving diverse countries and languages. Multilingual pretraining data includes manually created corpora, web-crawled data, and benchmark adaptations. Multilingual SFT data includes manually created datasets, machine-translated datasets, benchmark adaptations, and MLLM-aided generation. Multilingual RLHF data is used to train more effective reward models in multilingual contexts.
"Multilingual Large Language Models are capable of using powerful Large Language Models to handle and respond to queries in multiple languages, which achieves remarkable success in multilingual natural language processing tasks." "To this end, in this paper, we present a thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature."

Key Insights Distilled From

by Libo Qin,Qig... at 04-09-2024
Multilingual Large Language Model

Deeper Inquiries

How can MLLMs effectively handle the hallucination issue across different languages?

Hallucination in MLLMs refers to the generation of incorrect or misleading information by the model. To effectively handle this issue across different languages, MLLMs can implement the following strategies: Fact-Checking Mechanisms: Integrate fact-checking mechanisms that verify the accuracy of the generated content. This can involve cross-referencing information with reliable sources to ensure correctness. Language-Specific Training Data: Incorporate language-specific training data to improve the model's understanding of nuances and context in different languages. This can help reduce hallucination by providing more accurate language representations. Multi-Modal Verification: Implement multi-modal verification techniques where the model cross-references information across different modalities (text, image, audio) to validate the generated content. Fine-Tuning for Language Consistency: Fine-tune the model to prioritize language consistency and coherence, especially when generating content across multiple languages. This can help reduce hallucination by promoting language-specific accuracy. Continuous Monitoring and Feedback: Implement a system for continuous monitoring of model outputs and gathering feedback from users to identify and correct instances of hallucination promptly. By incorporating these strategies, MLLMs can effectively address the hallucination issue across different languages and improve the overall reliability of generated content.

What are the potential risks and challenges in ensuring the safety and fairness of MLLMs?

Ensuring the safety and fairness of MLLMs is crucial to prevent harmful outcomes and biases in the generated content. However, there are several potential risks and challenges in achieving this goal: Bias in Training Data: MLLMs may inherit biases present in the training data, leading to biased outputs that perpetuate stereotypes and discrimination across languages. Privacy Concerns: MLLMs may inadvertently expose sensitive or private information in generated content, posing privacy risks for individuals and organizations. Misinformation Propagation: MLLMs have the potential to amplify misinformation and fake news across languages, contributing to the spread of false information. Algorithmic Fairness: Ensuring fairness in MLLMs requires addressing algorithmic biases that may result in unequal treatment or outcomes for different languages or communities. Lack of Transparency: The opacity of MLLMs makes it challenging to understand how decisions are made, leading to difficulties in identifying and rectifying biases or safety issues. Legal and Ethical Implications: MLLMs raise legal and ethical concerns regarding accountability, liability, and the ethical use of AI technologies in generating content across languages. Addressing these risks and challenges requires a multi-faceted approach that involves robust data governance, algorithmic transparency, bias mitigation strategies, and continuous monitoring for safety and fairness in MLLMs.

How can MLLMs be extended to support more languages while preserving the performance of existing languages?

Extending MLLMs to support more languages while maintaining performance in existing languages involves the following strategies: Incremental Language Addition: Implement a gradual approach to adding new languages to MLLMs, ensuring that the model adapts to the linguistic characteristics of each language without compromising performance in existing languages. Adapter-Based Architecture: Utilize adapter-based architectures that allow for the efficient integration of new languages without retraining the entire model, preserving the performance of existing languages. Language-Specific Fine-Tuning: Conduct language-specific fine-tuning to optimize the model for each added language, ensuring that the performance in new languages is enhanced while maintaining high accuracy in existing languages. Cross-Lingual Knowledge Transfer: Implement cross-lingual knowledge transfer techniques to leverage the linguistic similarities between languages and facilitate the extension of MLLMs to new languages without sacrificing performance in existing ones. Continuous Evaluation and Monitoring: Regularly evaluate the performance of MLLMs across languages and monitor for any degradation in performance in existing languages due to the addition of new ones. Adjust the training and fine-tuning processes accordingly to maintain overall model performance. By employing these strategies, MLLMs can effectively expand to support more languages while upholding performance standards in existing languages, ensuring a comprehensive and high-performing multilingual model.