No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
arxiv(2024)
摘要
While the progression of Large Language Models (LLMs) has notably propelled
financial analysis, their application has largely been confined to singular
language realms, leaving untapped the potential of bilingual Chinese-English
capacity. To bridge this chasm, we introduce ICE-PIXIU, seamlessly amalgamating
the ICE-INTENT model and ICE-FLARE benchmark for bilingual financial analysis.
ICE-PIXIU uniquely integrates a spectrum of Chinese tasks, alongside translated
and original English datasets, enriching the breadth and depth of bilingual
financial modeling. It provides unrestricted access to diverse model variants,
a substantial compilation of diverse cross-lingual and multi-modal instruction
data, and an evaluation benchmark with expert annotations, comprising 10 NLP
tasks, 20 bilingual specific tasks, totaling 1,185k datasets. Our thorough
evaluation emphasizes the advantages of incorporating these bilingual datasets,
especially in translation tasks and utilizing original English data, enhancing
both linguistic flexibility and analytical acuity in financial contexts.
Notably, ICE-INTENT distinguishes itself by showcasing significant enhancements
over conventional LLMs and existing financial LLMs in bilingual milieus,
underscoring the profound impact of robust bilingual data on the accuracy and
efficacy of financial NLP.
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