Large language models usually evolve according to one of two scenarios. The first involves taking an off-the-shelf solution like GPT or Llama, fine-tuning it for specific tasks, and then launching it. The second approach is to build everything from scratch: collecting data, developing the architecture, training the model, and independently supporting its development. While the second path is much harder, it offers greater control and the ability to account for the specifics of language and culture.
LG AI Research chose exactly this approach by creating K-EXAONE – a multimodal model that works with both text and images, understands Korean at a native level, and incorporates cultural context. The project has been in development for several years, and recently, the team shared precisely how this system was built.
Why Build a Model from Scratch?
The main reason is language. Korean differs significantly from English, not just in grammar, but also in the logic of text construction, contextual nuances, and cultural references. Models trained primarily on English-language data can process Korean text, but they often don't do so with the desired accuracy and naturalness.
LG opted not to adapt someone else's model but instead decided to build its own, ensuring it would inherently understand language specifics and could be effectively applied in real Korean products and services. This applies not only to text but also to multimodality: the ability to work simultaneously with text and images, comprehending how they relate to each other.
What Is K-EXAONE?
K-EXAONE is a family of models capable of processing both text and images. Versions range in size from more compact ones to large ones capable of solving complex tasks. The model is trained on a vast volume of Korean and English data, allowing it to work with both languages, but with a particular emphasis on Korean.
The key difference is that it isn't just a language model, but a multimodal system. This means it can, for example, analyze an image and describe it in Korean, answer questions about a picture, or generate text based on visual context. For many applied tasks – from education to commercial services – this is an immensely important capability.
How Was the Model Built?
The process began with data preparation. LG collected a corpus of Korean texts from open sources, books, articles, and web pages. In parallel, data was prepared for multimodal training – image-text pairs that help the model understand the connection between visual and textual content.
The model architecture was developed in-house. It is a transformer model – the same basic approach used in GPT, Claude, and other systems, but with settings adapted to the specifics of the Korean language and multimodal functionality.
Training took place on LG's own infrastructure, utilizing a large amount of computing resources. After the base training, the model was fine-tuned on specialized data to improve its behavior in dialogues, increase answer accuracy, and enhance its safety.
Why LG Developed Its Own Language Model
Why Is LG Doing This?
LG isn't just home appliances; it's an entire ecosystem of products and services, from smart homes to business platforms. Having its own language model gives the company the ability to embed AI into its solutions without relying on external providers.
This is important not only from the perspective of technology control but also regarding data. By using its own model, the company can process information locally without transmitting it to third-party services. For corporate clients and users who prioritize privacy, this is a significant advantage.
Furthermore, the model can be tailored to specific tasks: from automating customer support to internal data analytics. This offers a level of flexibility that is hard to achieve when using off-the-shelf solutions.
Future Development Plans for K-EXAONE
What's Next?
LG continues to develop K-EXAONE. Plans include improving multimodal capabilities, expanding language support, and enhancing the quality of answers in complex scenarios. The model is already being used internally and could eventually become the foundation for public services.
An important point is openness. LG has released some versions of the model as open source, which allows researchers and developers to work with it, test it, and suggest improvements. This is a rare move for a major corporation, especially in Asia, where many technologies often remain proprietary.
K-EXAONE is an example of how a major company can build its own language model without relying on ready-made solutions. It is a long and resource-intensive path, but it provides control over the technology, the ability to account for cultural and linguistic features, and application flexibility. This is particularly relevant for the Korean market – and LG demonstrates that such an approach is entirely feasible.