KoNeoBench is a curated dataset designed to evaluate large language models’ understanding of Korean neologisms. It contains 1,785 recently attested Korean words from online news since 2020, each accompanied by usage examples, word‑formation analyses, and dictionary‑style definitions. The authors define four evaluation tasks, report results from recent models and a human baseline, and find that current LLMs struggle with recovering source components, distinguishing semantic categories, and generating accurate definitions.
By Soha Lee, Soojin Lee, Heesung Yang, Hyunju Song, Hyunji Lee, Jinsan An, Jeongwan Shin, Jin Hyun Park, Jun Lee, Hyeyoung Park, Kilim Nam
The paper investigates how large language models (LLMs) represent nine transborder ethnic communities along China’s southwestern border with mainland Southeast Asia, such as the Sami, Rohingya, and Kurds. It introduces the concept of identity ossification, where LLMs reduce fluid, cross-border identities to a single nation-state narrative, and presents the Transborder Identity Ossification Diagnostic Framework (TIODF) to differentiate between knowledge gaps and narrative framing. Experiments with GPT‑5.1 and DeepSeek‑V3.2 in English and Chinese show that ossification rates vary widely, with DeepSeek‑V3.2’s Chinese responses exhibiting the phenomenon in 62.6% of cases, indicating that narrative framing rather than mere knowledge absence drives the issue.
By Minglu Sun, Lan Chen, Hang Jiang
arXiv:2510.18368v2 Announce Type: replace
Abstract: We present $\textbf{Korean SimpleQA (KoSimpleQA)}$, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cu...
By Donghyeon Ko, Kyubyung Chae, Yeguk Jin, Byungwook Lee, Chansong Jo, Sookyo In, Jaehong Lee, Taesup Kim, Donghyun Kwak
arXiv:2609.00565v1 Announce Type: cross
Abstract: Cultural fine-tuning has become the de facto paradigm for building culture-aware large language models (LLMs), yet existing optimization exclusively...
By Jingshen Zhang, Shaoyang Xu, Wenxuan Zhang
arXiv:2608. 09925v1 Announce Type: cross Abstract: Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of public administration and the linguistic requirements of non-English contexts.
By Laurens Samson, Iva Gornishka, Gossa L\^o, Yuki M. Asano, Sennay Ghebreab
arXiv:2610.19399v1 Announce Type: new
Abstract: Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization. To enable systematic evaluation, w...
By SungHo Kim, Eojin Jeon, SangKeun Lee
KULTURE Bench is a new evaluation framework created to assess language models within the Korean cultural context. It includes datasets of cultural news, idioms, and poetry and tests models at word, sentence, and paragraph levels for cultural comprehension and reasoning. Experiments with models trained on different corpora show that current models still lack deep understanding of Korean cultural texts.
By Xiaonan Wang, Jinyoung Yeo, Joon-Ho Lim, Hansaem Kim
arXiv:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
By Haran Shani-Narkiss, Michael Fire, Oren Tsur
The paper introduces CultureLens, a benchmark with 9,261 scenarios from 75 countries that tests how large language models (LLMs) incorporate cultural information. Experiments on Llama‑3.1‑8B, Gemma‑3‑12B, and Qwen‑3.5‑27B show that presenting a contradictory cultural norm reduces aligned answers by 32‑47 percentage points, even when the model knows the correct country. Fine‑tuning with contradictory‑norm examples improves alignment to 85‑93% under contradictory norms while only slightly affecting performance under true norms.
By Samee Arif, Furkan Danisman, Angana Borah, Honglak Lee, Dehan Kong, Lajanugen Logeswaran, Moontae Lee, Zhijing Jin, Rada Mihalcea
The paper introduces CuCu, a multi‑agent LLM framework that converts national social studies curricula into open‑ended, culture‑specific question‑answer pairs for fine‑tuning language models. Using the Korean curriculum, the authors build KCaQA, a dataset of 34.1k QA pairs that cover culture‑specific topics and ground responses in local sociocultural contexts. Experiments show that fine‑tuning with KCaQA improves the model’s cultural alignment and relevance to Korean society.
By Haneul Yoo, Won Ik Cho, Geunhye Kim, Jiyoon Han
arXiv:2608. 07367v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly used as a primary source of information and advice, understanding their alignment to humans in terms of values becomes a pressing concern.
By Maria-Louisa Wightman, Guillaume Bied, Tijl De Bie
arXiv:2605. 14152v2 Announce Type: replace-cross Abstract: Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual safety is mostly assessed through translation-only benchmarks that preserve the underlying scenario, leaving how language and geopolitical context interact largely unexamined beyond a few language pairs.
By Michael S. Lee, Yash Maurya, Drew Rein, Bert Herring, Jonathan Nguyen, Kyungho Song, Udari Madhushani Sehwag, Jiyeon Cho, Kaustubh Deshpande, Yeongkyun Jang, Jiyeon Joo, Minn Seok Choi, Evi Fuelle, Christina Q. Knight, Joseph Brandifino, Max Fenkell