No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks

• URL: http://arxiv.org/abs/2403.06249v3
• Date: Fri, 16 Aug 2024 12:30:07 GMT
• Title: No Language is an Island: Unifying Chinese and English in Financial Large Language Models, Instruction Data, and Benchmarks
• Authors: Gang Hu, Ke Qin, Chenhan Yuan, Min Peng, Alejandro Lopez-Lira, Benyou Wang, Sophia Ananiadou, Jimin Huang, Qianqian Xie,
• Abstract summary: ICE-PIXIU uniquely integrates a spectrum of Chinese tasks, alongside translated and original English datasets. It provides unrestricted access to diverse model variants, a compilation of diverse cross-lingual and multi-modal instruction data, and an evaluation benchmark with expert annotations.
• Score: 75.29561463156635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 95k 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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