HAE-RAE Bench: Evaluation of Korean Knowledge in Language Models
This addresses the need for better evaluation of language models in Korean, though it is incremental as it builds on existing multilingual benchmarking efforts.
The paper tackles the problem of limited evaluation methodologies for large language models in non-English languages by introducing the HAE-RAE Bench, a dataset for Korean that challenges models lacking cultural and contextual depth, resulting in a benchmark that presents a greater challenge to non-Korean models.
Large language models (LLMs) trained on massive corpora demonstrate impressive capabilities in a wide range of tasks. While there are ongoing efforts to adapt these models to languages beyond English, the attention given to their evaluation methodologies remains limited. Current multilingual benchmarks often rely on back translations or re-implementations of English tests, limiting their capacity to capture unique cultural and linguistic nuances. To bridge this gap for the Korean language, we introduce the HAE-RAE Bench, a dataset curated to challenge models lacking Korean cultural and contextual depth. The dataset encompasses six downstream tasks across four domains: vocabulary, history, general knowledge, and reading comprehension. Unlike traditional evaluation suites focused on token and sequence classification or mathematical and logical reasoning, the HAE-RAE Bench emphasizes a model's aptitude for recalling Korean-specific knowledge and cultural contexts. Comparative analysis with prior Korean benchmarks indicates that the HAE-RAE Bench presents a greater challenge to non-Korean models by disturbing abilities and knowledge learned from English being transferred.