In the ever-evolving language industry landscape, a trend has emerged where executives are contemplating bypassing traditional translation processes and cutting budgets, swayed by the potential of Generative AI (GenAI).
As a localization professional, you and your team need to assume the position of experts in this field. I’ve prepared this Q&A to support localization managers facing pressure from various departments to integrate GenAI into their workflows. This guide aims to respond to the enthusiastic yet premature calls for adopting GenAI in place of established methodologies.
Keep in mind that the advancement of neural machine translation, which laid the groundwork for the transformer model, reminds us that we’ve been harnessing AI in our domain long before it became a buzzword in other departments. The following questions and answers shed light on the practicalities and current limitations of GenAI, while also underscoring the effectiveness of traditional localization processes. Give particular attention to the question about LQA, since this is an area where GenAI is succeeding.
To be clear, these are questions that you need to ask your colleagues when they propose using GenAI. The burden of proof should be on them, not on you.
Hallucination Check: “How do you plan to check for hallucinations in all the languages you use?”
Answer: Traditional localization processes don’t hallucinate and are proven to effectively manage such errors, being cost-effective and thoroughly tested compared to the still-developing GenAI.
Budget Allocation: “Who will be responsible for budgeting the implementation and ongoing costs of GenAI in translation processes?”
Answer: Traditional localization processes are not only tested but also tend to be more budget-friendly, ensuring effective allocation of resources without compromising quality. The localization department doesn’t have a budget to train LLMs nor for the tokens required by the new process, which are much more expensive than machine translation. If you are asked to used a new technology, you need to ask for MORE budget.
Contextual Accuracy: “How will you ensure contextual accuracy, especially in complex or specialized texts?”
Answer: The effectiveness and accuracy of traditional localization methods are unparalleled, especially in dealing with specialized texts, making them a more reliable choice.
Consistency Maintenance: “What strategies will be in place to maintain consistency across large volumes of translation?”
Answer: Traditional methods, backed by mature technologies like Machine Translation (MT) and Translation Memory (TM), ensure consistency more reliably than GenAI at its current stage.
Cost Implications: “Considering the potential hidden costs, are you sure GenAI will be more cost-effective in the long run compared to traditional methods?”
Answer: Traditional localization processes tend to be more cost-effective, avoiding hidden expenses that often accompany the deployment of newer technologies like GenAI.
Legal and Ethical Compliance: “How will GenAI handle the legal and ethical considerations, such as confidentiality and data protection in translation?”
Answer: Traditional methods have a solid track record of handling legal and ethical considerations efficiently, making them a safer choice compared to the nascent GenAI technology. This is especially true in regulated environments like Finance and Life Sciences.
Quality Control and AI’s Role: “What quality control measures will be implemented to guarantee the same level of precision as human translators?”
Answer: While traditional localization methods provide robust quality control, it’s noteworthy that GenAI is showing promising results in accelerating QA processes. Several companies are running successful pilots with GenAI in this area, although not yet at scale, indicating a potential future adoption. Do you want to fund a pilot for us to test this?
Brand Protection: “How will GenAI ensure the protection and accurate representation of brand identity and tone across languages?”
Answer: Traditional localization ensures brand protection by accurately capturing and representing brand identity and tone, a feat GenAI is still striving to achieve. We need to make sure that there are humans verifying accuracy, which we usually handle with our in-country partners.
Feedback Incorporation: “How will user feedback be incorporated to improve the translation quality over time with GenAI?”
Answer: Traditional localization methods are used to incorporating user feedback to continuously improve translation memories and style guides, whereas GenAI is still developing this capability.
In summary, although GenAI holds exciting potential for the future of language services, it’s crucial to balance enthusiasm with a realistic view of the current state of technology. For now, the reliability of traditional localization methods remains indispensable. Share your thoughts, questions, and experiences in the comments. Together, we can navigate toward a future where GenAI and human expertise synergize to enhance the richness of our linguistic landscape. And you can protect your team and your budget in 2024.