Industrial-Strength LLM Anthropic teamed up with South Korea’s largest mobile phone provider.

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Industrial-Strength LLM: Anthropic teamed up with South Korea’s largest mobile phone provider.

Anthropic, the startup behind the safety-focused Claude chatbot, teamed up with South Korea’s largest mobile phone provider.

What’s new: The independent research lab, which is an offshoot of OpenAI, will receive $100 million from SK Telecom to build a multilingual large language model tailored for the telecommunications industry, VentureBeat reported.

How it works: Anthropic will base the specialized model on the technology that underpins its large language model Claude. SK Telecom plans to offer it to other telecoms firms, such as members of the Global Telco AI Alliance, a consortium devoted to building new lines of business based on AI-driven services.

  • The model will be fine-tuned for telecoms applications like customer service, marketing, and sales.
  • It will support six languages: Korean, English, German, Japanese, Arabic, and Spanish.
  • Claude takes advantage of constitutional AI, a method designed to align large language models and human values based on a set of principles, or constitution. Initially, the model critiques and refines its own responses according to the constitution. Then it’s fine-tuned on the results via supervised learning. This is followed by a phase that Anthropic calls reinforcement learning from AI feedback, or RLAIF.

Behind the news: SK Telecom has a history of building its own machine learning models, particularly Korean-language models. The company emulated GPT-3's architecture to train models like Ko-GPT-Trinity-1.2B. An unidentified model enables A. (pronounced “a dot”), a virtual assistant for the company’s mobile users.

Why it matters: AI models have a bright future in virtually every industry, and specialized AI models have an even brighter outlook. Like BloombergGPT, this partnership represents a step toward adapting foundation models to a vertical industry, along with a new business model for good measure.

We’re thinking: Prompting a foundation model can go a long way in tasks for which it’s easy to write instructions that describe clearly what you want done. But many tasks involve specialized knowledge that’s difficult to put into a prompt; for instance, consider explaining how to draft a good legal document. In such cases, fine-tuning or specialized training can be a promising approach.


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