Korean Culture into LLM Alignment: Toward Cultural Coherence
About
Cultural-aspect work on large language models is dominated by a negative target: which outputs to suppress. We argue that a constructive counterpart is also needed, a working definition of what a culturally coherent response is rather than only what it must avoid, and instantiate it for Korean. We design an alignment-data pipeline around a prompt-based LLM seed generator that expands a Korean harm taxonomy, with a Korean-culturally-adapted safe-response policy at its centre: a per-category guideline grounded in Korean legal frameworks, social norms, and interpretive conventions, against which three frontier models each produce a candidate response. DPO fine-tuning on the resulting triplets improves the Korean cultural safe rate across six open-weight LLMs while causing no large degradation on Korean general-capability benchmarks, and qualitative outputs show fine-tuned models naming Korean statutes and institutional procedures and, where appropriate, supplying constructive Korean-context information alongside refusal.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| General Language Understanding | KMMLU | Overall Score57.45 | 16 | |
| Code Generation | HumanEval+ | Accuracy77.44 | 12 | |
| Mathematical Reasoning | HRM8K | Accuracy (%)48.3 | 12 | |
| Multi-turn Instruction Following | Ko-MT-Bench | Overall Score (1-10)8.21 | 12 | |
| Safety and Cultural Bias Evaluation | KoBBQ | Accuracy89.18 | 12 | |
| Safety Evaluation | Korset | Safe Rate88.97 | 12 |