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English(EN) Cross-Lingual Reusability of LLM Pipelines: Financial Extraction, Multilingual Reasoning, and Semantic Screening

LLM流水线展现出广泛的跨语言可重用性，但领域特定验证是关键

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新的arXiv论文研究了大型语言模型（LLM）流水线的跨语言可重用性，重点关注金融信息提取、多语言推理和语义筛选。研究发现，在英语中开发的流水线可以广泛地跨语言重用，准确率损失极小，其中Gemini 3.5 Flash实现了高提取率。然而，推理工作量和语言难度排名在一般知识问答和金融内容分析之间存在显著差异，这表明每个领域、每种语言的验证仍然至关重要。 AI

影响 这项研究表明，LLM流水线可以更轻松地跨不同语言部署，从而可能降低开发成本并加速全球采用。

排序理由 该集群包含一篇在arXiv上发表的学术论文，详细介绍了LLM流水线的研究成果。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

LLM流水线展现出广泛的跨语言可重用性，但领域特定验证是关键

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该集群包含一篇在arXiv上发表的学术论文，详细介绍了LLM流水线的研究成果。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Joong Ho (Joshua), Choi, Sherine George, Jiayang Zhao, Jayesh Rathi, Ashwin Kandath, Ananya Sane, Vishvak Srinivasan ·

    LLM管线跨语言可复用性：金融信息提取、多语言推理与语义筛选

    arXiv:2610.27043v1 Announce Type: cross Abstract: Financial institutions increasingly deploy large language models (LLMs) in multilingual markets, raising a practical question: can pipelines developed and validated in English be reused across languages, or must they be rebuilt pe…

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