Evaluating Polish linguistic and cultural competency in large language models
원문 초록
Large language models (LLMs) are becoming increasingly proficient in processing and generating multilingual texts, which allows them to address real-world problems more effectively. However, language understanding is a far more complex issue that goes beyond simple text analysis. It requires familiarity with cultural context, including references to everyday life, historical events, traditions, folklore, literature, and pop culture. A lack of such knowledge can lead to misinterpretations and subtle, hard-to-detect errors. To examine language models' knowledge of the Polish cultural context, we introduce the Polish linguistic and cultural competency benchmark, consisting of 600 manually crafted questions. The benchmark is divided into six categories: history, geography, culture & tradition, art & entertainment, grammar, and vocabulary. As part of our study, we conduct an extensive evaluation involving over 30 open-weight and commercial LLMs. Our experiments provide a new perspective on Polish competencies in language models, moving past traditional natural language processing tasks and general knowledge assessment.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
TaxoAdapt: Aligning LLM-Based Multidimensional Taxonomy Construction to Evolving Research Corpora
Improving Personalized Search with Regularized Low-Rank Parameter Updates
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts
BnMMLU: Measuring Massive Multitask Language Understanding in Bengali
토론
이 논문과 연결된 포럼 글이 아직 없습니다.