TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts
This research addresses the problem of accurately evaluating embedding models for low-resource languages, such as Korean, which is significant for organizations and individuals working with financial texts in these languages.
The authors tackled the problem of evaluating embedding models in low-resource languages and found that directly translating established benchmarks fails to capture linguistic and cultural nuances, with their novel benchmark KorFinMTEB revealing critical discrepancies in model performance. The study highlights the limitations of direct translation, with models performing robustly on translated benchmarks but struggling with tasks requiring deeper semantic understanding on KorFinMTEB.
Domain specificity of embedding models is critical for effective performance. However, existing benchmarks, such as FinMTEB, are primarily designed for high-resource languages, leaving low-resource settings, such as Korean, under-explored. Directly translating established English benchmarks often fails to capture the linguistic and cultural nuances present in low-resource domains. In this paper, titled TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Models Bring? A Case Study on Korea Financial Texts, we introduce KorFinMTEB, a novel benchmark for the Korean financial domain, specifically tailored to reflect its unique cultural characteristics in low-resource languages. Our experimental results reveal that while the models perform robustly on a translated version of FinMTEB, their performance on KorFinMTEB uncovers subtle yet critical discrepancies, especially in tasks requiring deeper semantic understanding, that underscore the limitations of direct translation. This discrepancy highlights the necessity of benchmarks that incorporate language-specific idiosyncrasies and cultural nuances. The insights from our study advocate for the development of domain-specific evaluation frameworks that can more accurately assess and drive the progress of embedding models in low-resource settings.