The development of large language models (LLMs) has historically been English-centric. While this has often proved successful, it has struggled to capture the richness and diversity of global languages. This issue is particularly pronounced with languages such as Korean, which boasts unique linguistic structures and deep cultural contexts. Nevertheless, the field of artificial intelligence (AI) research and development has seen a noticeable shift towards more inclusive and culturally-aware models, with efforts being made to create LLMs more attuned to different linguistic environments.
Notable examples of models designed for English text generation include GPT-3 by OpenAI, while multilingual frameworks such as mT5 and XLM-R have broadened the capabilities of LLMs. Language-specific models such as BERTje and CamemBERT have been created for Dutch and French respectively, and there are even models like Codex that incorporate code generation. Korean-focused models like KR-BERT and KoGPT represent a move towards developing LLMs sensitive to specific cultural and linguistic contexts.
One of the models leading the charge in this new wave of culturally-aware AI is HyperCLOVA X, developed by researchers from NAVER Cloud’s HyperCLOVA X Team. Unlike its predecessors, this model focuses on both the Korean language and culture while also maintaining proficiency in English and programming code. Its innovation lies in its balanced use of Korean and English data, alongside programming code. This mixture of data is then refined via instruction tuning against high-quality, human-annotated datasets, and under strict safety guidelines.
Key to HyperCLOVA X’s methodology is its integration of group-query attention and rotary position embeddings into transformer architecture, thus enhancing understanding of context and training stability. The model then underwent Supervised Fine-Tuning (SFT), where it learned from human-annotated demonstrations, and Reinforcement Learning from Human Feedback (RLHF) to align its outputs with human values.
HyperCLOVA X achieved 72.07% accuracy in comprehensive Korean benchmarks, setting a new standard for Korean language understanding. It also achieved a 58.25% accuracy rate on English reasoning tasks and managed a 56.83% success rate in coding challenges. These figures highlight HyperCLOVA X’s innovative character, drawing together multilingual comprehension and application-specific performance. Crucially, it also indicates a significant advance in AI’s linguistic and cultural adaptability.
The research introduces HyperCLOVA X as a significant step forward in culturally aware AI technologies. In addition to its impressive linguistic capabilities, a significant focus was placed on safety, to ensure alignment with ethical guidelines and cultural nuances. These factors, together with the remarkable language understanding and coding benchmarks, are verifying that linguistically and culturally diverse AI models, such as HyperCLOVA X, are paving the way forward in AI research and development.
The researchers fully deserve credit for this breakthrough. The shift from English-centric models will only increase AI’s global adaptation and utilization.
