K or NAT : LLM Alignment Benchmark for K orean Social Values and Common Knowledge (2024)

Abstract: To reliably deploy Large Language Models (LLMs) in a specific country, they must possess an understanding of the nation’s culture and basic knowledge. To this end, we introduce National Alignment, which measures the alignment between an LLM and a targeted country from two aspects: social value alignment and common knowledge alignment. We constructed KorNAT, the first benchmark that measures national alignment between LLMs and South Korea. KorNat contains 4K and 6K multiple-choice questions for social value and common knowledge, respectively. To attain an appropriately aligned ground truth in the social value dataset, we conducted a large-scale public survey with 6,174 South Koreans. For common knowledge, we created the data based on the South Korea text books and GED exams. Our dataset creation process is meticulously designed based on statistical sampling theory, and we also introduce metrics to measure national alignment, including three variations of social value alignment. We tested seven LLMs and found that only few models passed our reference score, indicating there exists room for improvement. Our dataset has received government approval following an assessment by a government-affiliated organization dedicated to evaluating dataset quality.

## introduction large language models (llms) @xcite @xcite * equal contribution. ## dataset construction this section provides a detailed explanation of kor-nat construction. a visual overview of the dataset creation process is shown in figure 2 . samples from the dataset can be found in appendix b.11 and c.5. after the creation, our dataset passed both qualitative and quantitative reviews by tta, an organization tasked by the korean government for reviewing the dataset quality. ## national alignment score social value alignment. assigning a single ground truth label based on the majority vote may ignore valuable information in the responses on other options @xcite @xcite . therefore, we use the distribution of responses from the survey to measure the social value alignment. let r ij be the ratio of participants choosing the j-th option for the i-th question, q i . if a model predicts the k-th option for q i , it receives an alignment score of r ik . thus, the model earns a score between 0 and 1 for each question. the final social alignment score is the average across all questions. we call this metric social value alignment (sva). intuitively, a model achieving a score higher than 0.5 would mean that it aligns with the majority of the korean population. with sva, however, the maximum achievable score is empirically calcu-lated as 0.450. this indicates variability in social values within the korean population of a given question using the five levels of agreement. to alleviate this problem, we introduce aggregated social value alignment (a-sva) with modified ground truth distributions. for a-sva, the ground truth distribution is narrowed down to three options by aggregating 'strongly disagree' with 'disagree', and 'strongly agree' with 'agree'. by a-sva, the maximum achievable score increases to 0.626, suggesting a moderate level of agreement among korean citizens. as a third metric, we additionally propose neutral-processed social value alignment (n-sva), because it can be argued that choosing 'neutral' is more suitable for questions with no significantly preferred opinions. for n-sva we maintain the five options but change it into a neutral one-hot distribution if neither of the aggregated options surpass a value of 0.5. ## conclusion in this paper, we introduce national alignment from two perspectives: social value alignment and common knowledge alignment. we constructed kor-nat consisting of 10k samples, along with proposing national alignment scores with three variations in social value alignment. our dataset has been approved by the government through the evaluation by tta. we are planning to launch a public leaderboard in june 2024, allowing model evaluations on our dataset. the complete data is scheduled for release on ai hub foot_4 in december 2024.