update citation

README.md

@@ -30,7 +30,7 @@ Nemotron-Personas-Japan は、日本のモデル開発者が重要な地域固
 
 Nemotron-Personas-Japan is an open-source (CC BY 4.0) dataset of synthetically-generated personas grounded in real-world demographic, geographic, and personality trait distributions in Japan to capture the diversity and richness of the population. It is a variant of [Nemotron-Personas](https://huggingface.co/datasets/nvidia/Nemotron-Personas), which is the first dataset of its kind aligned with statistics for names, sex, age, background, marital status, education, occupation and location, among other attributes. This version of the dataset provides high-quality personas for a variety of modeling use-cases in Japanese.
 
-Nemotron-Personas-Japan supports Japanese model builders in developing [Sovereign AI](https://www.nvidia.com/en-us/lp/industries/global-public-sector/sovereign-ai-technical-overview/) systems that incorporate important region-specific demographics and cultural context. The dataset improves diversity of synthetically-generated data, mitigates biases, and prevents [model collapse](https://medium.com/data-science/addressing-concerns-of-model-collapse-from-synthetic-data-in-ai-7cd380208d14) (degradation caused by uncurated training on another model’s outputs) by reflecting Japan's real geographic and demographic distributions. In particular, the dataset is designed to be more representative of underlying demographic distributions along multiple axes, including age (e.g. older personas), geography (e.g., rural personas), education, occupation, etc., as compared to past persona datasets. As an example, one can produce high-quality multi-turn chat conversation data with real names, ages, occupation, cultural and education backgrounds, all of which bring unique perspectives and angles to that data. 
+Nemotron-Personas-Japan supports Japanese model builders in developing [Sovereign AI](https://www.nvidia.com/en-us/lp/industries/global-public-sector/sovereign-ai-technical-overview/) systems that incorporate important region-specific demographics and cultural context. The dataset improves diversity of synthetically-generated data, mitigates biases, and prevents [model collapse](https://medium.com/data-science/addressing-concerns-of-model-collapse-from-synthetic-data-in-ai-7cd380208d14) (degradation caused by uncurated training on another model's outputs) by reflecting Japan's real geographic and demographic distributions. In particular, the dataset is designed to be more representative of underlying demographic distributions along multiple axes, including age (e.g. older personas), geography (e.g., rural personas), education, occupation, etc., as compared to past persona datasets. As an example, one can produce high-quality multi-turn chat conversation data with real names, ages, occupation, cultural and education backgrounds, all of which bring unique perspectives and angles to that data. 
 
 Produced using [NeMo Data Designer](https://docs.nvidia.com/nemo/microservices/latest/generate-synthetic-data/index.html), an enterprise-grade compound AI system for synthetic data generation, the dataset leverages a proprietary Probabilistic Graphical Model (PGM) along with an Apache-2.0-licensed GPT-OSS-120B model and an ever-expanding set of validators and evaluators built into Data Designer. An extended version of Nemotron-Personas-Japan is available for use in NeMo Data Designer itself.
 
@@ -71,7 +71,7 @@ Nemotron-Personas-Japan データセットは、日本の国勢調査におけ
 
 The Nemotron-Personas dataset is intended to be used by the community to continue to improve open models and push the state of the art. The data may be freely used to train any model. We welcome feedback from the open-source community and invite developers, researchers, and data enthusiasts to explore the dataset and build upon it.
 
-The Nemotron-Personas-Japan dataset is grounded in distributions of self-reported demographic data in the Japanese census. As such, its primary goal is to support Sovereign AI development by combating missing data and/or potential biases present in model training data today, especially when it comes to existing persona datasets used in synthetic data generation. Despite the improved data diversity and fidelity to Japan’s population, we are still limited by data availability and reasonable model complexity. This results in some necessary independence assumptions; for instance, that occupations are independent of education given location (prefecture) and sex. Similarly, comprehensive statistics on gender, independent of sex, are not available from the Japan Census. We leave further efforts to improve fidelity to future work.
+The Nemotron-Personas-Japan dataset is grounded in distributions of self-reported demographic data in the Japanese census. As such, its primary goal is to support Sovereign AI development by combating missing data and/or potential biases present in model training data today, especially when it comes to existing persona datasets used in synthetic data generation. Despite the improved data diversity and fidelity to Japan's population, we are still limited by data availability and reasonable model complexity. This results in some necessary independence assumptions; for instance, that occupations are independent of education given location (prefecture) and sex. Similarly, comprehensive statistics on gender, independent of sex, are not available from the Japan Census. We leave further efforts to improve fidelity to future work.
 
 Note that the dataset is focused on adults only.
 
@@ -103,7 +103,7 @@ The dataset contains:
 * ~1.4B tokens total, including ~850M persona tokens 
 * Comprehensive coverage across demographic, geographic, and personality trait axes
 * ~950k unique names
-* 1500+ occupation categories reflecting Japan’s workforce
+* 1500+ occupation categories reflecting Japan's workforce
 * A variety of persona types: professional, sports, arts, travel, culinary
 * Natural language persona attributes: cultural background, skills & expertise, goals & ambitions, hobbies & interests.
 
@@ -152,14 +152,14 @@ The analysis below provides a breakdown across various axes of the dataset to em
 氏名 (Names)
 本データセットはペルソナに焦点を当てているため、氏名は専用のフィールドとしては提供されていません。しかし、ペルソナ合成には、[名字由来net](https://myoji-yurai.net/) によって提供された 20,000 件のユニークな名前と 97,000 件のユニークな姓が組み込まれています。
 
-Since the focus of this dataset is on personas, names aren’t provided as dedicated fields. However, infused into persona generation are 20,000 unique first names, 97,000 unique last names provided by [Myoji-Yurai.net](http://Myoji-Yurai.net).
+Since the focus of this dataset is on personas, names aren't provided as dedicated fields. However, infused into persona generation are 20,000 unique first names, 97,000 unique last names provided by [Myoji-Yurai.net](http://Myoji-Yurai.net).
 
 ## 年齢分布 (Age Distribution)
 日本のペルソナにおける年齢分布は、国全体の実際の人口構造を反映しており、中高年層が大きな割合を占め、若年層に向かって徐々に減少していくという特徴があります。この分布では若者が少なく、第一次および第二次ベビーブーム世代に大きな膨らみがあります。さらに、日本の女性は世界的に見ても非常に長寿であり、高齢者に占める女性の割合は高くなっています。
 
 なお、このデータセットには 18 歳未満の未成年は含まれていません。
 
-The age distribution of our Japanese personas mirrors the country’s real demographic structure, characterized by a large proportion of people in the middle to older age groups and a gradual decline toward the younger cohorts. The distribution shows fewer young people and a significant expansion in the generations born during the baby boom and the second baby boom. Furthermore, Japanese women are exceptionally long-lived by global standards, and the proportion of women among the elderly is high. 
+The age distribution of our Japanese personas mirrors the country's real demographic structure, characterized by a large proportion of people in the middle to older age groups and a gradual decline toward the younger cohorts. The distribution shows fewer young people and a significant expansion in the generations born during the baby boom and the second baby boom. Furthermore, Japanese women are exceptionally long-lived by global standards, and the proportion of women among the elderly is high. 
 
 Note that minors under 18 are excluded from this dataset.
 <center>
@@ -185,7 +185,7 @@ The heatmap below captures patterns of educational attainment across age cohorts
 ## 学歴の地理的特徴 (Geographic Intricacies of Education Attainment)
 このデータセットの一部は、地理的要因が教育水準に影響を与え、ひいてはペルソナの記述にも反映されることを示しています。コロプレスマップは、各都道府県ごとに25歳以上の住民のうち学士号以上を取得している人の割合を示しています。私たちの検証では、いかなるLLMもこの精度のデータを生成することはできませんでした。
 
-This slice of our dataset demonstrates how geography informs education and therefore persona descriptions. The choropleth map shows, for each prefecture, the share of residents ages 25 and older who hold at least a bachelor’s degree. No LLM in our testing was able to generate data of this fidelity.
+This slice of our dataset demonstrates how geography informs education and therefore persona descriptions. The choropleth map shows, for each prefecture, the share of residents ages 25 and older who hold at least a bachelor's degree. No LLM in our testing was able to generate data of this fidelity.
 <center>
   <img src="images/nemotron_personas_japan_education_map.png" width="700px">
 </center>
@@ -247,7 +247,7 @@ Please report security vulnerabilities or NVIDIA AI concerns [here](https://www.
 If you find the data useful, please cite:
 ```
 @software{nvidia/Nemotron-Personas-Japan,
-  author = {Fujita, Atsunori and Gong, Vincent and Ogushi, Masaya and Suhara, Yoshi and Corneil, Dane and Meyer, Yev},
+  author = {Fujita, Atsunori and Gong, Vincent and Ogushi, Masaya and Yamamoto, Kotaro and Suhara, Yoshi and Corneil, Dane and Meyer, Yev},
   title = {{Nemotron-Personas-Japan}: Synthetic Personas Aligned to Real-World Distributions},
   month = {September},
   year = {2025},