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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
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A survey of multilingual large language models
Libo Qin1, Qiguang Chen2, Yuhang Zhou2
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Patterns (New York, N.Y.)
|February 3, 2025
Summary
This paper surveys multilingual large language models (MLLMs), offering a unified taxonomy and identifying emerging research frontiers. It provides resources to accelerate breakthroughs in MLLM development and application.
Area of Science:
- Natural Language Processing
- Artificial Intelligence
- Machine Learning
Background:
- Multilingual large language models (MLLMs) demonstrate significant success in processing queries across diverse languages.
- A comprehensive survey of existing MLLM approaches and recent advancements is currently lacking.
Purpose of the Study:
- To present a unified and thorough review of the field of multilingual large language models.
- To highlight recent progress, identify emerging trends, and discuss challenges in MLLM research.
Main Methods:
- Conducting an extensive survey of multilingual alignment techniques in MLLMs.
- Developing a unified taxonomy to categorize and summarize current MLLM progress.
- Identifying and discussing key emerging frontiers and associated challenges in the field.
Main Results:
- The paper provides the first comprehensive review of multilingual alignment in MLLMs.
- A novel, unified framework is introduced for summarizing MLLM research.
- Key emerging research frontiers and their challenges are identified.
Conclusions:
- This work offers a pioneering review of multilingual large language models, consolidating existing knowledge.
- The provided taxonomy and identified frontiers aim to guide future research and development in MLLMs.
- Abundant open-source resources are collected to facilitate community access and accelerate breakthroughs.
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