TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? - A Case Study on Korea Financial Texts

2025-02-10

AI Summary

Existing embedding benchmarks predominantly target high-resource languages and fail to support deep semantic understanding in low-resource languages—such as Korean—within domain-specific contexts like finance. Direct translation of English benchmarks (e.g., FinMTEB) overlooks linguistic structures and cultural specificity, leading to evaluation distortion. Method: We introduce KorFinMTEB, the first Korean financial-domain-specific embedding benchmark, grounded in a culturally aware, task-localized evaluation paradigm for low-resource settings. It covers diverse tasks—including retrieval, classification, and clustering—validated via human annotation and expert labeling, and rigorously assessed for cross-lingual transfer bias. Contribution/Results: Experiments reveal substantial performance degradation of state-of-the-art embedding models on KorFinMTEB, with fine-grained semantic tasks exhibiting up to 37% higher error rates. These findings underscore the irreplaceable role of localized, domain-specific benchmarks in advancing embedding models for low-resource scenarios.

Technology Category

Natural Language Processing: Lexical Semantics and MorphologyMachine Learning: Large Multimodal Models (LMMs)Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsGraph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsWeb Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Low-resource Korean financial texts
• Domain-specific embedding models
• Cultural and linguistic nuances in benchmarks

Innovation

• Developed KorFinMTEB benchmark
• Tailored for Korean financial texts
• Captures language-specific cultural nuances