ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning
Evaluates ChatGPT's zero-shot performance across 37 languages and 7 tasks, revealing limitations in multilingual capabilities.
Key Findings
Methodology
The paper evaluates ChatGPT's zero-shot learning performance across 37 languages and 7 tasks, including NER, POS tagging, and QA. Languages are categorized by resource levels using CommonCrawl data, and single-stage prompts simulate typical user interactions.
Key Results
- Result 1: ChatGPT achieved 84.5% average accuracy in POS tagging across 17 languages, outperforming XLM-R's 79.3%.
- Result 2: ChatGPT underperformed supervised models in NER tasks for low-resource languages.
- Result 3: English prompts consistently outperformed target-language prompts, highlighting ChatGPT's bias toward English.
Significance
This study highlights ChatGPT's limitations in multilingual tasks, particularly for low-resource languages. It underscores the need for language-specific models and further research in multilingual NLP.
Technical Contribution
This is one of the largest multilingual evaluations of ChatGPT, covering 37 languages and multiple tasks. It introduces single-stage prompts and systematically compares English versus target-language prompts.
Novelty
First comprehensive evaluation of ChatGPT's multilingual performance across high-, medium-, low-, and extremely low-resource languages, revealing biases and limitations.
Limitations
- Limitation 1: ChatGPT performs poorly on low-resource languages, especially for complex tasks.
- Limitation 2: Zero-shot learning outputs are highly sensitive to prompt design, leading to instability.
Future Work
Future research could explore improved prompt designs, specialized models for low-resource languages, and further assessments of societal impacts.
AI Executive Summary
ChatGPT has gained widespread attention for its impressive language generation capabilities, but its performance in multilingual tasks remains underexplored.
This paper evaluates ChatGPT's zero-shot learning across 37 languages and 7 tasks, spanning high-, medium-, low-, and extremely low-resource languages. Results show strong performance in high-resource languages but significant weaknesses in low-resource languages and complex tasks, highlighting its bias toward English.
The findings emphasize the need for language-specific models and improved prompt designs, providing valuable directions for future research and applications in multilingual NLP.
Deep Analysis
Background
Large language models like GPT-3 and ChatGPT have revolutionized NLP, but their multilingual capabilities remain largely unevaluated, especially for low-resource languages.
Core Problem
The core problem is assessing ChatGPT's performance in multilingual tasks, particularly its ability to handle low-resource languages, which is critical for advancing equitable NLP technologies.
Innovation
Key innovations include: 1) Comprehensive evaluation across 37 languages; 2) Single-stage prompt design to simulate user interactions; 3) Comparison of English versus target-language prompts.
Methodology
- �� Categorize languages by resource levels using CommonCrawl data
- �� Design zero-shot prompts with task descriptions and output format specifications
- �� Evaluate 7 tasks, including POS tagging, NER, and QA
- �� Compare ChatGPT's performance with supervised models
Experiments
Experiments use multilingual datasets like XGLUE-POS and MultiCoNER. ChatGPT's zero-shot performance is compared against supervised models using metrics like accuracy.
Results
ChatGPT outperformed XLM-R in POS tagging but underperformed in NER for low-resource languages. English prompts consistently yielded better results than target-language prompts.
Applications
Findings can inform the development of multilingual NLP applications like translation systems, QA tools, and information extraction systems.
Limitations & Outlook
ChatGPT struggles with low-resource languages and is sensitive to prompt design. Its zero-shot learning capabilities are insufficient for complex tasks.
Plain Language Accessible to non-experts
Imagine ChatGPT as a multilingual translator who is great at English but struggles with less common languages. It tries its best but often misses details when the language resources are scarce.
ELI14 Explained like you're 14
ChatGPT is like a super-smart robot that knows many languages but is best at English. If you ask it tough questions in rare languages, it might get confused! Cool, right?
Glossary
Zero-shot Learning
The ability of a model to perform tasks without prior training.
Used to evaluate ChatGPT's multilingual capabilities.
POS Tagging
Assigning syntactic labels to words in a sentence.
Tests basic grammar understanding of language models.
NER (Named Entity Recognition)
Identifying entities and their types in text.
Evaluates ChatGPT's handling of complex language tasks.
CommonCrawl
A large-scale web corpus.
Used to classify language resource levels.
Prompt Design
Crafting inputs to optimize model outputs.
Essential for zero-shot learning evaluations.
Open Questions Unanswered questions from this research
- 1 How can ChatGPT's performance in low-resource languages be improved?
- 2 What role does prompt design play in output quality?
Applications
Immediate Applications
Multilingual Translation
ChatGPT can handle high-resource language translations but needs optimized prompts.
QA Systems
Useful for English-based QA tasks but limited for rare languages.
Long-term Vision
Language-specific Model Development
Develop specialized models for low-resource languages to ensure fairness in NLP.
Abstract
Over the last few years, large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) that fundamentally transform research and developments in the field. ChatGPT represents one of the most exciting LLM systems developed recently to showcase impressive skills for language generation and highly attract public attention. Among various exciting applications discovered for ChatGPT in English, the model can process and generate texts for multiple languages due to its multilingual training data. Given the broad adoption of ChatGPT for English in different problems and areas, a natural question is whether ChatGPT can also be applied effectively for other languages or it is necessary to develop more language-specific technologies. The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i.e., beyond reported anecdotes), which is still missing or limited in current research. Our work aims to fill this gap for the evaluation of ChatGPT and similar LLMs to provide more comprehensive information for multilingual NLP applications. While this work will be an ongoing effort to include additional experiments in the future, our current paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources. We also focus on the zero-shot learning setting for ChatGPT to improve reproducibility and better simulate the interactions of general users. Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages, calling for further research to develop better models and understanding for multilingual learning.