Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs
  Between Spanish and English
Xiao Zhang Ruoyu Xiang Chenhan Yuan Duanyu Feng Weiguang Han Alejandro Lopez-Lira Xiao-Yang Liu Sophia Ananiadou Min Peng Jimin Huang Qianqian Xie
Despite Spanish's pivotal role in the global finance industry, a pronounced
gap exists in Spanish financial natural language processing (NLP) and
application studies compared to English, especially in the era of large
language models (LLMs). To bridge this gap, we unveil Tois\'on de Oro, the
first bilingual framework that establishes instruction datasets, finetuned
LLMs, and evaluation benchmark for financial LLMs in Spanish joint with
English. We construct a rigorously curated bilingual instruction dataset
including over 144K Spanish and English samples from 15 datasets covering 7
tasks. Harnessing this, we introduce FinMA-ES, an LLM designed for bilingual
financial applications. We evaluate our model and existing LLMs using FLARE-ES,
the first comprehensive bilingual evaluation benchmark with 21 datasets
covering 9 tasks. The FLARE-ES benchmark results reveal a significant
multilingual performance gap and bias in existing LLMs. FinMA-ES models surpass
SOTA LLMs such as GPT-4 in Spanish financial tasks, due to strategic
instruction tuning and leveraging data from diverse linguistic resources,
highlighting the positive impact of cross-linguistic transfer. All our
datasets, models, and benchmarks have been released.