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Fine-Tuning LLMs for Multilingual Applications

Technology today makes it easy to compete in the global marketplace. It’s not uncommon for businesses to expand halfway around the globe. Even if you’re operating in a smaller geographical area, you probably deal with clients from different countries.

Many other companies are facing the same issue. Therefore, we’re seeing a lot more multilingual applications coming out.

However, how good these applications are depends on how well you train them. Therefore, in this post, we’ll look at fine-tuningfor multilingual LLMs, and how to get it right.

What is LLM Fine-Tuning in this Context?

Using an LLM gives you a head-start. You begin with a generically trained model that learns from trillions of data points online. Then you’ll have to optimize it for your needs.

You’ll need to teach the model by feeding it information that relates to various languages or specific linguistic tasks. These include:

  • Translation
  • Sentiment analysis
  • Summarization in multiple languages

Challenges in Fine-Tuning LLMs for Multilingual Applications

So, is this type of project easy? Training an LLM in this field poses some interesting challenges. Think of it like teaching a child a new skill like holding a spoon. At first, the child watches you. They’ll give it a try and usually fail miserably.

They can’t get it right the first time because they need to work out how to hold the spoon. Over time, they practice and work it out by finding patterns.

Your LLM needs you to feed it the right information. The spoon, in this case, is a language dataset. You’ll need to give your model enough practice with high-quality data.

Now let’s see what you have to watch for with LLM fine-tuning in this use case.

Data Imbalance

You may need help finding general-purpose training data in some languages. This won’t be an issue with English, French and Spanish. However, you’ll have problems with dialects and less-common languages like Malagasy and Xhosa.

You’ll need to work out how to augment your training dataset to combat this issue.

Diverse Grammar and Syntax Structures

Each language has its own idiosyncrasies. For example, in English, you’d say, “I throw a rock at him.” In Afrikaans, however, the sentence structure is different. Translated directly from Afrikaans, the same sentence reads, “I threw him with a rock.”

These are distinctions that an LLM may struggle with. You’ll need to use specific training data to overcome this.

Computational Costs

The best way to learn to speak a language is to practice with a native speaker. The ideal way to train an LLM is to feed in as much data as possible. However, that can be expensive, especially if you pre-translate the content first.

However, pre-translating the data is unnecessary with models like PaLM2.

Techniques for Fine-Tuning LLMs for Multilingual Applications

Here are some ways to overcome these challenges.

Multilingual Data Augmentation

What happens with the less common languages there isn’t much data for? You can augment the data using techniques like back-translation: Here, you translate a sentence from one language back to another to create variations.

Domain-Specific Fine-Tuning

Are you in healthcare, finance, or any other industry that uses specialist phrases? If so, you need to use domain-specific data to train your model.

Cross-Lingual Transfer Learning

With this approach, you teach your model shared concepts between high and low-resource languages. For example, Spanish and Portuguese use a similar syntax. You could use these languages to help train similar low-resource languages.

Language-Specific Tokenizers

You can break your text down into smaller units to improve its efficiency. However, you have to adapt with languages like Japanese or Chinese. In these cases, the script doesn’t allow for segmentation.

Adapter Modules

You add an adapter to an LLM’s architecture to zero its focus without retraining the whole model. You can train the adapters on language-specific data. Doing this allows you to save resources.

Use of Language-Specific Pretraining

Don’t have enough source information? Start training your model using monolingual data to give your model a solid understanding. Then fine-tune it with more specific information in the relevant languages.

Best Practices for Fine-Tuning Multilingual LLMs

You’ll need to do more than make technical adjustments. Before you proceed, consider these things.

Define Clear Evaluation Metrics

You need to establish clear goals before you begin. Then you should establish language and task-specific evaluation metrics. You can look at things like:

  • BLEU scores
  • F1 scores
  • Cross-lingual accuracy

These are all good metrics for translation and general language tasks. You must benchmark across languages to identify areas for improvement.

Optimize for Low-Resource Languages

It would help if you prioritized the fine-tuning for less common languages first. Doing things this way around prevents overfitting. Overfitting occurs when a model performs well during training but badly in reality. In this case, it usually does better with common languages.

Regularly Update Training Data

Think of the terms we use today, like cellphone, desktop, or web search. A century ago, no one would have understood these terms. Today, Gen Z probably has yet to learn what Betamax is.

The point is that language evolves over time. You need to retrain your models as slang and other terms change.

Balance Data Across Languages

You want your model to perform well overall. You should use techniques like sampling and weighting to balance the dataset across all languages. This way you can ensure that the less common languages receive enough attention.

Monitor Cultural Sensitivity

You don’t want to offend your clients by using inappropriate or offensive language. You should look into region-specific fine-tuning and carefully evaluate the results.

Implement Human-in-the-Loop Validation

Tools like scalable annotation automation can speed up training. However, you should always use human oversight. Work with a team from the right local area. They’ll pick up on small things that make your app come across as more natural.

Conclusion

Fine-tuning your LLM for multi-language applications can be hard. If you plan properly, however, you’ve got a head start. Simply watch out for things like data imbalances and look at ways to supplement your training database.