ChatGPT Translation: How to Harness AI to Drive Global Business Growth

Seamless communication across many languages is mission-critical for a successful global organization.

Companies vying for the attention of the global user, or those with a multilingual and multicultural workforce will be exposed to the importance of translation and localization at an early stage of their growth trajectory. 

While it can produce impactful content, traditional human-centric translation can sometimes be insufficient in terms of time-to-market and scalability. 

With such enormous potential to upend content generation, is ChatGPT the answer businesses have been hoping for when it comes to translations?

In this article, we’ll explore ChatGPT’s capabilities, advantages, and limitations for translation within global businesses.

What is ChatGPT?

Let’s start with the basics. ChatGPT(short for “Chat Generative Pre-trained Transformer”) is a large language model-based (LLM) chatbot and virtual assistant designed to generate human-like text, based on the input it receives in the form of a user-defined command or “prompt.” 

ChatGPT is arguably the most well-known representative of this category of language AI applications, but certainly not the only one: Claude, Google Gemini, Microsoft Copilot, Perplexity AI, DeepL and Llama are some other popular alternatives.

As a neural machine translation (NMT) system, ChatGPT leverages deep learning techniques to understand context, nuances, and the subtleties of human language.

Although it is far from perfect, each new iteration has shown significant advancements – with a few caveats, which we’ll explore later in this article

Initially developed for natural language understanding and generation, ChatGPT’s use cases encompass a variety of potential scenarios, and translation is one of them.

But just how well-suited is it for translation?

Can ChatGPT be used for translation?

The short answer is yes, ChatGPT can be used for translation.

Its ability to process and generate text in multiple languages allows it to translate content with a degree of fluency and coherence. 

ChatGPT uses its understanding of language patterns to provide contextually appropriate translations. 

However, while using ChatGPT for translation is pretty straightforward (all you need to do is to “ask” it to translate), it does have its limitations, particularly in a business context. 

What is ChatGPT translation good for?

One of the most appealing aspects of ChatGPT is that any business or individual worker can tap into its capabilities for translation.

The barrier to entry for translating content has never been so low. However, this also means that there is greater scope for inaccurate or incomplete translations, so there is a real need for a measured approach to avoid business risks. 

Whether it’s used for translating business documents, emails, customer support queries, or website content, ChatGPT could be considered a compelling solution due to its flexibility and efficiency.

The model can be integrated into existing content creation workflows via APIs and web interfaces, or it can be embedded into chatbots, making it accessible across different platforms and in real-time.

Again, this ease of implementation is attractive, but brings significant risk if not managed correctly. 

Let’s consider where using AI—as opposed to relying on the traditional human-centric process—can boost efficiency and save cost:

1. Text documents: Organizations can use ChatGPT to translate complete reports, manuals, and other text-heavy documents that would otherwise never be localized due to the sheer volume of content (and the high cost of localizing them).
2. Email communications: AI can be beneficial for both external and internal communications.Customer support teams can respond to queries in the customer’s native language. But they may struggle with the nuance required for more in-depth or commercially sensitive communications.
3. Internal communications: ChatGPT can support non-confidential staff communications from unilingual (often in English) to multilingual, improving employee engagement and experiences.
4. Long-tail website content: There’s nothing more frustrating for a user than to not find the information they are looking for in their native language and have to rely on the English version.However, typically, only the top-performing articles and primary languages receive attention for translation.
   Businesses can use GenAI to translate the long tail of their website content for different regions, such as FAQs or knowledge based articles.  This way, AI also becomes a tool for accessibility, enhancing the overall user experience and boosting engagement.
5. User-generated content: Popular platforms such as Reddit, Twitch, and others house user-generated content that some brands would benefit significantly from having available in multiple languages.
   So far, this content has rarely received translation treatment, partly due to its prohibitive cost.By leveraging LLM-powered solutions to localize the content, companies could unlock access to new user communities. 

   However, it is important to realise that, while community marketing promises to be one of the most important marketing fronts in the next few years, it is an area that requires a deep understanding of particular communities.A (very) human voice is needed to be truly effective. So, while you can utilize AI in certain use-cases, it’s important to understand the risks involved, and the oversight required.

Which companies are already using GenAI?

Numerous companies have integrated generative AI (GenAI) solutions into their translation workflows, with varying levels of success.

For every company publicly announcing what they are doing with the tech, dozens more are actively experimenting with it, but may not yet be ready to share the results. 

It takes time to gain the necessary support internally, test it, and fully deploy an AI solution, but this isn’t stopping almost every company from trying to determine whether GPT-like applications are viable.

Let’s review a few examples of companies that have gone on the record about using ChatGPT and similar applications to produce multilingual content for their users. 

Reddit is embracing LLM-based translation

In May 2024, Reddit’s CEO shared how they are testing LLM-based translations for user-generated content housed on the platform for their French users, with a view on eventually doing so in other languages.

Reddit is betting on language AI to help drive international growth and user engagement.

AI as a tool to expand education at Khan Academy

Khan Academy, a renowned non-profit organization, uses the GPT-4-based virtual assistant they dubbed Khanmigo to deliver engaging and effective learning for students and support to teachers.

This enables them to reach a broader audience and enhance their impact.

Indeed creates multilingual jobs postings using AI

To help more people get jobs, Indeed evolved their content production pipeline from a fully human workflow to one that begins with AI. 

They’ve been using LLMs to generate multilingual content from scratch instead of translating the original copy into multiple languages, and then passing it on to human editors for quality assurance and fine-tuning.

Phrase uses AI to transform translation and localization processes

Phrase uses GenAI in its localization tech to automate complex tasks and enhance efficiency.

It improves machine translation quality, reduces human editing, and automates quality assurance, saving up to 90% on costs. 

Open AI’s latest GPT model is also integrated into Phrase’s machine translation engine as a secure, fully supported translation engine.

The ability to integrate glossaries and support tag handling ensures superior quality machine translation of content for any department across an organization.

Key considerations before translating with ChatGPT

Let’s be clear about this—testing and deploying ChatGPT in your systems will take careful planning. It will also involve getting multiple stakeholders on board, from legal, tech, IT, marketing, and product. 

Relying solely on ChatGPT to translate content straight out of the box without fine-tuning and expecting it to provide human-level translation quality is unrealistic and can even risk severely damaging brand equity.

This is due to the technology’s inherent limitations—it simply wasn’t built with translation in mind.

GPT-like applications are not a magic bullet for translating your content, so it is imperative for any business producing multilingual content for a global audience to be aware of their limitations.

Some of these limitations can be surmounted with a carefully designed process or integrating with the right technology that securely combines the power of the machine with human oversight and expertise.

• Accuracy when handling complex or specialized content: While ChatGPT excels in general translation, it can struggle with highly technical or domain-specific content.Including human oversight, with subject matter experts (e.g., professional linguists or in-house personnel) reviewing key high-visibility content is necessary to ensure a precise result. 
• Hallucinations: Language models are tuned to produce results (or say “yes” to whatever you ask of them) and have been known to produce inaccurate results or downright confabulations (frequently referred to as “hallucinations”).Training the model on high-quality data is a must to avoid this. 
• The human should remain part of the loop: AI translations, including those by ChatGPT, can miss the mark in terms of cultural nuances or idiomatic expressions.Integrating with specialist translation and localization technology that  automatically highlight a address these concerns, can help reduce this risk.Human linguistic reviews for high priority translated content can also help mitigate these issues and result in a higher fidelity output.

Data privacy and security are paramount

Before attempting to translate anything using ChatGPT, make sure you get the all-clear from your legal department about using ChatGPT or similar solutions that rely on data for training, self-improvement, and producing results. 

This should be your first step when deliberating whether to use ChatGPT for translation. 

You certainly want to prevent your employees from using the publicly available version of ChatGPT (or Google Translate) and inputting sensitive or proprietary information. 

Relying on robust data security measures, such as end-to-end encryption and secure API connections, helps safeguard critical information during translation. 

While using GPT-like applications for translation is appealing, companies must be aware of their potential and limitations and plan accordingly. 

AI technology is continually evolving, which means that deploying it will not be a single action. 

The pace of development means you will need to invest time and resources to stay on top of the latest advancements and continually test your approach.

Ensuring translation excellence when using automated translation

Regardless of whether you opt for ChatGPT for translation or not, quality should remain at the top of your mind. 

Quality requirements will differ depending on the specific purpose of the content and its type. 

Some content, such as user-generated product reviews, may not require a high degree of polish and can be processed by ChatGPT, whereas your marketing slogan will likely require multiple pairs of human eyes to get right. 

The rule of thumb is to let the purpose of the content dictate the human vs. machine vs. machine-augmented-human approach.

When considering automated translation solutions, one parameter that distinguishes GenerativeAi from machine translation (MT) is its ability to understand context, significantly improving the accuracy of the content. 

The language model can retain contextual information over longer passages, making it adept at providing coherent translations that consider the broader narrative. This is particularly important for content where meaning can shift significantly based on context. 

In addition, ChatGPT can continuously learn from new data, improving over time. Organizations can contribute to this learning process by providing translation feedback and incorporating custom training data that reflects their specific needs and language use cases.

However, remember that ChatGPT’s learning is only as good as the data it is being given. Relying on a public GPT can significantly increase the risk of low-quality or tonally inconsistent translations. 

While many GenAI providers offer a closed-loop ‘professional’ solution to combat this, you will still face issues at scale, especially if you are translating a variety of assets that require a different tonal approach. 

For example, marketing collateral may require a friendly, informal translation, while a customer help documentation may require straightforward, accurate and formal language to minimize misunderstandings. 

Making this differentiation requires a careful, standardized approach to prompt design.  This can involve additional effort and an intense Q&A process, which may negate some of the efficiency you are striving for in the first place. 

When assessing the viability of the output provided by ChatGPT, a best practice is to use a combination of industry-standard metrics such as BLEU, hLepor, or COMET that have been used for scoring machine translation (MT) engines and human evaluation. 

If all this sounds complicated, ask your localization tech provider support team or your peers to provide guidance on setting up and running an AI quality pilot project.

Beyond translation: scaling your operations with AI-powered tools

While ChatGPT offers a potentially useful translation solution that can be integrated into various systems, true excellence in translation quality and consistency requires more specialized technology.

For those seeking the pinnacle of translation excellence, the fusion of the latest OpenAI GPT model with advanced machine translation provides exceptional translation quality.

Furthermore, the integration of glossaries and automated tag handling significantly enhances accuracy and consistency.

This ensures your translations are not only precise but also aligned with your brand’s terminology, providing a seamless and professional experience across all your communications.

It’s also worth bearing in mind that AI can help with much more than just translating your content. For instance, a frequent use case for language AI is automating language quality assurance (LQA) processes. 

At Phrase, AI is at the heart of our technology and has been for several years. AI is key to how we are revolutionizing traditionally labor-intensive LQA processes. 

For example, by introducing Phrase Quality Performance Score (Phrase QPS) and Auto LQA, leveraging AI’s ability to instantly assess the quality of content that has already been translated and suggesting translators varied ways of improving it. 

Then, there is AI’s ability to orchestrate and manage translation workflows, a critical area where program managers can look for efficiencies.

AI-powered applications can streamline and automate repetitive tasks such as preparing a translation file handoff and allow your employees to focus on value-added tasks such as internal alignment and strategic planning.

Again though, this will require a more complex technology integration to ensure security and consistency, and isn’t something you can leave entirely to a single GPT-like system

Translating content with ChatGPT is just one of many potential use cases of generative AI. 

AI technology shines when used in collaboration with other cutting-edge technology,  as a tool to improve efficiencies and augment human capabilities, allowing the humans in the loop to use their expertise to validate the content and focus on designing memorable user experiences.

As you progress through the different stages toward deploying AI in real-life production scenarios, it’s important to remember that the tools will be only as good as the data you use to train them. 

Following the best practices outlined below ensures you receive the most contextually accurate and nuanced translations when using GPT-like applications.

1. Look after data quality: Use high-quality, representative data for training to ensure the model learns accurately.
2. Establish a feedback loop: Provide regular feedback on translations to help the model improve over time. A simple and lightweight means to ensure this would be to add a button that would rate the resulting translation, whereby your internal users can score the output and thus help it to improve.
3. Domain-specific training: The more detailed, domain-specific data you include, the better. 

Think of it this way. The public-facing version of ChatGPT that any user can use is pretty generalist, and you may get limited mileage out of it if you want to be specific. 

One of the best ways to improve a language model is to provide domain-specific data.

Your own data will not be extensive enough to provide consistent, high-quality results from an LLM trained on the general internet. 

Is ChatGPT the right choice for translation?

Overall, ChatGPT offers convenience for one-off or ad-hoc translations, but its limitations become evident at scale, particularly regarding consistency and quality. 

For large-scale enterprise translation needs, leveraging a more comprehensive, built-for-purpose solution is crucial. 

The right solution will combine the strengths of multiple AI engines, including ChatGPT, to deliver high-quality, consistent translations.

This will give you a more robust platform that supports extensive automation, customization, and integration, ensuring reliable and scalable localization. 

By taking a more nuanced approach and seeing ChatGPT as part of a wider translation ecosystem, you’ll significantly reduce risk while maximizing efficiency. This in turn will maintain the integrity of your brand’s voice across all languages.

We’re here to help you get ready for an AI-infused future

The rise of language AI applications represents a paradigm shift in the organization’s ability to manage multilingual communication. 

Deploying these models, provided you do so with care and work around their current limitations, can help break down language barriers within the company, and between the company and its user base, facilitating smoother interactions and ultimately improving the user experience.

Your organization’s translation needs will evolve and scale as you expand into new markets and attract new customers. 

Different stages of growth will require different translation solutions, whether human-centric or AI-augmented. 

Keep an open mind and experiment with different solutions for various content types to identify the right mix of human and machine for your budget. 

At Phrase, we understand the business of tomorrow will rely on AI to transform legacy processes and provide real-time, optimized user experiences. 

We’ve been embracing AI for many years and have developed AI-powered functionality  which is at the core of the  Phrase Localization Platform. It combines the best of both worlds—human expertise and AI’s ability to streamline processes and enhance your reach. 

We’re giving you the capabilities to translate content using AI while benefiting from industry-leading technology and quality assurance solutions while minimizing risks.