arXiv Computation and Language
The paper introduces KBS, a Korean Benchmark Suite that includes six datasets—Ko-ARC, Ko-GSM8K, Ko-EQ-Bench, Ko-WinoGrande, Ko-LAMBADA, and Ko-IFEval—covering nine capabilities across 9,396 items. It emphasizes a non‑translation approach by reviewing, localizing, and redesigning tasks for Korean, and evaluates Korean‑developed, multilingual open, and closed API models under protocols that consider model access, prompt format, and scoring differences. The results highlight that Korean benchmark scores are influenced by task construction, scoring rules, prompting, and post‑training, suggesting that evaluations should report both final scores and the settings used.
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:2610.19638v1 Announce Type: new
Abstract: We present J-tau, the first publicly available Japanese benchmark for evaluating tool-using LLM agents in interactive customer-support scenarios. While...
By Chihiro Yano, Jun Hirako, Hiroaki Yamagiwa, Ryota Hirobuchi
We introduce NOLLI, a procedurally generated English-Korean puzzle benchmark designed to diagnose where Korean performance gaps arise. It comprises 15 puzzle types (25 tasks; 7,500 items), with every instance seed-regenerable, verified to have a unique solution, and scored deterministically.
Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them. These choices can substantially affect measured...
arXiv:2604.19782v2 Announce Type: replace-cross
Abstract: Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LAL...
By Jinyoung Kim, Hyeongsoo Lim, Eunseo Seo, Minho Jang, Keunwoo Choi, Seungyoun Shin, Ji Won Yoon
arXiv:2610.19185v1 Announce Type: new
Abstract: Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them....
By Shenbin Qian, David Samuel, Jind\v{r}ich Helcl, Andrey Kutuzov, Stephan Oepen
arXiv:2610.09152v1 Announce Type: new
Abstract: Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation....
By Marek \v{S}uppa, Ivan Vykopal, Andrej Ridzik, Kristi\'an Sopkovi\v{c}, Nat\'alia K\v{n}a\v{z}ekov\'a, Jaroslav Kop\v{c}an, Miroslav Bl\v{s}t\'ak, Vikt\'oria Ondrejov\'a, Daniel Hl\'adek, Michal Gregor, Martin Tamajka, Mari\'an \v{S}imko
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...
arXiv:2609.35820v1 Announce Type: cross
Abstract: English-only benchmarks expose only a narrow slice of voice-agent behavior. We introduce $\tau$-Multilingual, extending $\tau$-Voice to Spanish, Braz...
By Soham Ray, Edgard dos Santos Paiva, Ruben Valenzuela, Karthik Narasimhan, Keshav Dhandhania, Victor Barres
arXiv:2610.17205v1 Announce Type: new
Abstract: LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a ty...
By Jonathan Geuter, Andrew Cheng, David Alvarez-Melis
arXiv:2609.18156v1 Announce Type: new
Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
By Jianan Wu
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y