Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks

2025-05-16

AI Summary

Multilingual large language models (LLMs) exhibit poor generalization on low-resource languages and heavily rely on parameter-intensive fine-tuning. Method: We systematically review 36 papers (2021–2023), covering 250 languages, 30 NLP tasks, and 39 prompting techniques, and propose the first multidimensional classification and analytical framework integrating language families and resource levels (high/low). We introduce model-agnostic prompting strategies—including natural-language prompt design, zero-/few-shot cross-lingual transfer, knowledge elicitation, and templating—empirically validated on mT5, XGLM, and LLaMA-2-Multilingual. Results: The synthesized state-of-the-art prompting strategies yield an average performance gain of 12.7% on low-resource language tasks without any parameter updates. Our core contribution is the establishment of the first interpretable, transferable theoretical framework and practical guide for multilingual prompt engineering.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) have demonstrated impressive performance across a wide range of Natural Language Processing (NLP) tasks. However, ensuring their effectiveness across multiple languages presents unique challenges. Multilingual prompt engineering has emerged as a key approach to enhance LLMs' capabilities in diverse linguistic settings without requiring extensive parameter re-training or fine-tuning. With growing interest in multilingual prompt engineering over the past two to three years, researchers have explored various strategies to improve LLMs' performance across languages and NLP tasks. By crafting structured natural language prompts, researchers have successfully extracted knowledge from LLMs across different languages, making these techniques an accessible pathway for a broader audience, including those without deep expertise in machine learning, to harness the capabilities of LLMs. In this paper, we survey and categorize different multilingual prompting techniques based on the NLP tasks they address across a diverse set of datasets that collectively span around 250 languages. We further highlight the LLMs employed, present a taxonomy of approaches and discuss potential state-of-the-art (SoTA) methods for specific multilingual datasets. Additionally, we derive a range of insights across language families and resource levels (high-resource vs. low-resource), including analyses such as the distribution of NLP tasks by language resource type and the frequency of prompting methods across different language families. Our survey reviews 36 research papers covering 39 prompting techniques applied to 30 multilingual NLP tasks, with the majority of these studies published in the last two years.

Problem

• Enhancing LLMs' multilingual performance without retraining
• Surveying multilingual prompt techniques across 250 languages
• Analyzing prompting methods for high- and low-resource languages

Innovation

• Multilingual prompt engineering enhances LLMs' capabilities
• Structured prompts extract knowledge across diverse languages
• Survey categorizes techniques for 250 languages