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DeepL, the Germany-based startup that’s been leading the way on AI translation technology, yesterday released its next-generation model. The company was valued at $2 billion in a recent funding round and has made quite a splash in the translation subset of the AI boom, stating that its technology is already used by over 100,000 businesses, governments, and other organizations. But believe it or not, this is DeepL’s first model to be based on the large language model technology that’s underlined the AI hype and just about every AI product out there.
“We’ve been working with similar neural networks since DeepL was conceived in 2017, but there's been obviously an evolution in how this architectures works, and for the first time, we've moved that toward LLM technology,” CEO Jarek Kutylowski told Eye on AI, adding that “LLMs are going to be taking over translation.”
The model is not a general LLM, such as GPT-4 or Llama 3. It was built entirely by DeepL in-house, on its own infrastructure, and is tailored specifically for translation. The unnamed model —”We’re not doing names,” Kutylowski said—is meant to do everything the company’s previous model was doing, only better, plus hopefully even more down the road. The company hopes the new LLM architecture will pave the way for new capabilities such as multimodal translation and the ability to translate text into multiple languages simultaneously in real-time as the user types.
Anyone can use DeepL’s translation model via its website and various browser extensions and software integrations, but the company is specifically targeting global companies that want to translate everything from Slack messages to sensitive legal documents. Many translation tools only offer the ability to write in your own language and then prompt the model to translate your text in its entirety, but DeepL’s model also includes interactive writing features, suggesting different options for how you might want to phrase something so you can ensure it will be linguistically correct in the other language while still making it your own.
“You want to make sure that this is your text and that even if translated, this is something that you've written,” Kutylowski said. “Kind of fine-tuning it, making it yours, is an important thing that our users value.”
As with all AI software, data is key. And it is clear that DeepL views its access to top-class translation data, as well as the humans it has in the loop of its training process, as key differentiators. DeepL uses a combination of publicly available data (which expert linguists assess for quality so only the “right” data is actually fed into the model), as well as licensed datasets and synthetic data. Human experts provide feedback on the model outputs in order to improve its performance, a process referred to as reinforcement learning through human feedback (RLHF). According to Kutylowski, the jump in quality from the human feedback is “quite significant” and DeepL wouldn’t be where it is without it.
Of course, the company has significant competition from Big Tech, with all the regular players including Microsoft, Google, Apple, Amazon, and Meta investing in similar technologies. As the Semafor Technology newsletter has written, “companies like Google can snap their fingers and get the ability to translate more than 100 languages and offer that technology for free.” In total, DeepL supports 32 languages with its previous generation software (only English, Japanese, German, and Simplified Chinese so far with the next generation), while Google’s recent additions bring Google Translate to a whopping 243 languages.
Leading AI companies such as OpenAI are getting in on the race, too. Earlier this week, Cohere also announced a “significant investment” and strategic partnership with Japanese tech giant Fujitsu to build LLMs with Japanese language capabilities for global enterprises.
Still, DeepL believes its models provide the best quality translations. The company said that in blind tests, language experts preferred its next-generation model 1.3 times more often than Google Translate, 1.7 times more often than ChatGPT-4, and 2.3 times more often than Microsoft. DeepL, however, has not published this research and declined to offer specifics on the methodology such as how many language experts were involved in the tests. In 2021, an independent evaluation found DeepL to be the highest-performing machine translation model across 13 language pairs, outperforming Google and 16 others.
While many use cases for AI are still being debated, the benefits of using AI for translation are easy to see. Businesses are becoming more global by the day. It’s also an area where there’s a lot to gain with little to lose. Few professionals had access to human translators to begin with, but now everyone—from growing companies to vacation travelers—has a translator in their pocket that can open up entirely new opportunities.
“From my perspective, it's really incredibly exciting. And it was not clear for quite some time if LLMs were going to be the way forward technologically when it comes to translation,” Kutylowski said. “I think right now in this step, it really shows that they are capable of outperforming other models, but also, what I’m really excited about is the additional capabilities we will be able to infuse into those models in addition to the normal translation that you know nowadays, just because [LLMs] are not only capable of translation but can do more.”
And with that, here’s more AI news.
Sage Lazzaro
sage.lazzaro@consultant.fortune.com
sagelazzaro.com
