Evaluating Multilingual Language Models: The English Dilemma

In today's world, multilingualism is not just appreciated; it’s a necessity. With countless languages spoken around the globe, the demand for effective communication tools in various languages is skyrocketing. This is where language models (LMs) come into play. They are fancy computer systems designed to understand and generate human language. But how do we evaluate their performance across different languages, and what role does English play in this situation?

#The Growing Interest in Multilingual Language Models

As technology advances, the interest in multilingual Natural Language processing (NLP) is growing. Researchers are racing to develop models that can handle multiple languages, leading to the creation of numerous tools, benchmarks, and methods. However, one language tends to dominate the conversation: English.

English is often used in multilingual evaluations of language models. This isn’t just a coincidence; it’s because there isn’t enough instruction data available in many other languages. So, what happens? English sneaks its way into the mix, acting as a kind of bridge between the model and the different languages.

#Two Roles of English in Evaluations

English takes on two key roles in multilingual evaluations. The first is as an Interface, and the second is as a natural language.

#English as an Interface: Task Performance Over Language Understanding

Think of English as the translator who helps the model understand what it needs to do. When researchers want to test how well a language model performs on a specific task, they often use English prompts. For example, if you want a model to classify news topics in various languages, you might ask it to do so in English first. This method has its perks—like getting better results—but it raises an important question: Are we really testing the model's understanding of other languages?

Using English as an interface focuses on improving task performance. This means the goal is to get the best results, even if it means mixing languages in an unnatural way. This is sometimes called a mixed-prompt, where English is combined with another language.

Imagine asking a multilingual model to classify a news item in Turkish, but you provide the instructions in English. The result might be accurate, but does it really show the model understands Turkish? This kind of setup can lead to biased evaluations, making it difficult to gauge a model's true capabilities.

#English as a Natural Language: Aiming for Language Understanding

In contrast, when English behaves like any other spoken language, it helps produce genuine results reflecting a model's understanding. This is what we call using English as a natural language. When researchers evaluate multilingual models using prompts fully in the target language or natural code-switching, we can get a clearer picture of how well the model understands each language.

For instance, if you ask the model questions in Dutch, it should respond in Dutch without English creeping in to help it along. This approach aligns with the goal of multilingual natural language understanding (MLU). It acknowledges that understanding a language means truly grasping its nuances, not just relying on English as a crutch.

#The Mixed-Prompt Dilemma: A Balancing Act

Using mixed prompts has become a common practice in the evaluation of multilingual models. However, this method comes with its flaws. When we mix English with another language, we introduce additional factors that can cloud the evaluation results.

For example, imagine a model answering questions about a subject where the prompt is in English but the questions are in Spanish. This setup tests not only how well the model knows Spanish but also how well it can understand English prompts. Thus, the results can be misleading. Instead of clearly evaluating the multilingual capabilities, researchers may also be inadvertently testing the model's English proficiency.

#Methodologies in Multilingual Evaluation

Researchers have developed various methodologies for evaluating multilingual models. These range from having prompts entirely in the target language to using English commands alongside task-specific content in the target language. However, none of these methods genuinely solve the problem of mixed prompts.

For example, consider a setup where the prompt instructs the model in English while the content it needs to analyze is in another language. This technique can lead to significant gaps in understanding, and it often causes confusion about what is actually being evaluated.

Whether the prompts are presented fully in a target language or a mixture of English and another language, it remains crucial to design evaluation methods that truly reflect a model's multilingual understanding rather than simply its ability to follow English instructions.

#Implications of Using English in Evaluations

The implications of using English in multilingual evaluations can be far-reaching. Evaluations that rely heavily on English can lead to Knowledge Leakage. This term refers to the way certain knowledge from English can seep into the evaluation process, ultimately skewing the results.

When English is treated as a programming language, it may feel like we're using a universal code to operate the multilingual model. However, since English is also a natural language, its usage in mixed prompts can complicate things. This results in evaluating more than just the target language task; it also assesses how well the model understands English instructions. If the model can’t grasp the instructions in English, it might struggle even in languages where it should excel.

#The Importance of Natural Language

Evaluating multilingual models in a way that genuinely reflects their ability to understand different languages is vital. While mixing English in evaluations may lead to higher task performance, it can also obscure what our models can really do.

In a multilingual environment, researchers should strive for methods that treat all languages equally. Using native prompts in the target language or code-switching that feels natural can help improve evaluation practices. This way, researchers can obtain valid results reflecting the model's true abilities in every language it claims to handle.

#Moving Forward: A Call for Change

In summary, English plays a dual role in evaluating multilingual language models: it can serve as an interface to improve task performance, but it can also function as a natural language that supports real understanding. While there are clear benefits to using English as an interface, the trade-off is not insignificant.

To improve multilingual evaluations, we should shift our focus away from treating English as a tool for boosting performance. Instead, we should aim for methods that result in a true understanding of each language the model is meant to engage with.

#Conclusion: The Future of Multilingual Language Model Evaluations

As we look to the future, the aim should be clear: we must be more thoughtful in our approach to evaluating multilingual language models. By recognizing the distinct roles English plays in evaluations, we can work towards methods that genuinely reflect a model's understanding.

We don’t want to evaluate models like we’re playing a game of language hopscotch, where English acts as a safety net. Instead, we should strive for a fair playing field where all languages get the respect and attention they deserve. After all, language learning isn’t just about knowing a few words; it’s about understanding a culture, a context, and, most importantly, the people who speak it.

So, let’s embrace the beautiful mess that is multilingualism and challenge ourselves to get our evaluations right. With the right approach, we can make sure that our evaluations are not only effective but also genuinely reflect the rich tapestry of our world's languages.