Korean Culture into LLM Alignment: Toward Cultural Coherence
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 17 of its 24 references that are in the map. First posted on arXiv 5 Jun 2026.
Builds on
17 of 24 references.
Closest ancestors · 2
Same direction, newest first
- 2024KorNAT: LLM Alignment Benchmark for Korean Social Values and Common Knowledge21 Feb
- 2023Cultural Bias and Cultural Alignment of Large Language Models23 Nov
Other references · 15
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
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- 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,720
- 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,651
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
- 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 505
- 2024OR-Bench: An Over-Refusal Benchmark for Large Language Modelscited by 244
- 2025X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agentscited by 125
- 2024KMMLU: Measuring Massive Multitask Language Understanding in Koreancited by 91
- 2023KoBBQ: Korean Bias Benchmark for Question Answeringcited by 71
- 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 53
- 2025Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gapcited by 43
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2024Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models
- 2025Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMs
- 2025Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition
- 2023CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models
- 2025Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
- 2024Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models
- 2025CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs
- 2023HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models