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Updated: May 29, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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.

Keywords:

cross-lingual transfer large language model multilingual alignment multilingual large language model parameter-frozen alignment parameter-tuning alignment

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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.