Lately, we’ve been sharing some of our 2024 predictions for the translation industry. One of our predictions is that AI-powered technology will become even more attractive to companies and will essentially become a requirement to hold a competitive advantage in the industry. 2023 was a year that showed both the extraordinary capabilities of Generative AI, specifically Large Language Models, but it also revealed many shortcomings and the need for carefully planned development and deployment decisions.
In 2024, we will likely see the impact on the translation industry grow exponentially, touching all stages of the localization journey. While the focus to this point has been on text transformation and generation, the development of multimodal models could start to impact areas like multimedia localization, voice over, and image translation too.
We enjoyed Olga’s predictions so much that we decided to ask a few more of our colleagues to reflect on how LLMs have impacted their roles, the translation industry as a whole, and their expectations for the coming year.
Let’s see what they had to say:
LLMs generated a new inflection point of automation for the entire translation industry. Fundamental problems in automating language transformations are finally within reach of being partially or fully solved via these new technologies.
LLMs have profoundly changed my role, but even more importantly, they have dramatically changed our entire industry. As I look into next year, my goal is: how do I best advise the client, i.e. of the myriad of possibilities we can use LLMs for, which ones provide the highest value and move the dial the furthest for the cost that is incurred.
LLMs make automated translation smarter, faster, and more effective than ever before. These leaps in technology are allowing our translators to work more efficiently than ever before. Linguists can upskill and expand their careers by embracing these tools. Our data shows that productivity is up and that linguist income has not taken the hit many had feared it would.
LLMs have revolutionized the way my team approaches email creation. By aggregating data from various sources about the prospect and the company's objectives, LLMs empower my team to act as editors rather than doing content creation from scratch.
After the initial hype, it became clear that LLMs are really good at specific tasks and very valuable for certain Use Cases, but they aren’t generalists and won’t replace neural MT just yet, especially when MT workflows are also leveraging Glossary, TM and/or custom-trained engines. Hallucinations, costs and the need to engineer a separate prompt to get the desired results for each use case are currently limiting wide scale adoption of LLMs in many organizations.
I have noticed many attempts to incorporate Language Model Models (LLMs) in various business fields, but not all of them have been successful. If you know how to ask the right questions, you can achieve excellent outcomes. They are still just an assistant, albeit a highly intelligent one.
60% of the engineering team uses ChatGPT or GitHub Copilot daily. We have found that Trivial prompts are significantly less stable and performant than sophisticated ones, which undergo multiple iterations of testing and refinement. I am curious to see whether prompts ultimately become the subject of IP. We are also keeping an eye on smaller LLMs like LLama and Alpaca that can offer cost management and in-house (smaller) models. LLMs are getting very expensive at large volumes/scale.
It’s clear that LLMs have had a big impact on Smartling over the past year as we’ve embraced its use in both our product and in day-to-day roles. Overall, we definitely can call 2024 “The Year of Responsible AI”, where the benefits of innovation will be harvested globally in a responsible, ethical and most efficient way. We look forward to partnering with our customers to explore these new capabilities together this year.