Hangzhou (China)-based e-commerce and tech conglomerate Alibaba has announced that it has released a proprietary large language model (LLM) that is “better than products offered by Google, DeepL, and ChatGPT.” Shots fired.

The proprietary LLM — known as “Marco MT” — is to be deployed across Alibaba’s existing translation offering launched last year to small and medium-sized buyers and merchants looking to translate product listings, product descriptions, and other business communications.

Kaifu Zhang, International Vice President at Alibaba, said “we’ve had the [AI] system for about a year, and have half a million adoptions and more than 100M API calls per day. Basically, we’re seeing real value from our investment in AI.”

“The announcement [of Marco MT] is a component of our AI stack. […] We just updated our translation infrastructure with a large language model. Previously it was based on the previous generation of natural language processing (NLP) and machine learning algorithms, but now we have completely revamped it,” he said.

The update follows research led by Alibaba earlier this year, which measured neural machine translation output against large language model output for e-commerce content.

2024 Slator Pro Guide: Translation AI

The 2024 Slator Pro Guide presents 20 new and impactful ways that LLMs can be used to enhance translation workflows.

The company enables merchants to sell products worldwide by translating live chat conversations and product information — including text within images — into 29 languages, for USD 12 per 1 million characters. The solution includes optimized data for e-commerce, as well as glossary management and personalized customizations to the model’s output.

Eyeing International Expansion

The company is looking for international growth in several key markets, including the US, Europe, Brazil, and the Middle East following investments in Trendyol.

Zhang explained, “A lot of our merchants come from China, Southeast Asia, or Turkey, and when they’re trying to sell to Europe or the Middle East, they face challenges in terms of language barriers, understanding the local market, having the right access to talent, and [producing] good enough marketing messages. All of this translates into conversion rates for those merchants and into the bottom line.”

“By applying our language model, [merchants] get better translation quality, especially for some of the less represented languages. And that’s where the traditional and conventional NLP tools fail to deliver,” he added.