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arXiv 2408.02302cs.CL
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SNFinLLM:中文大语言模型的系统化且细致入微的金融领域适配

SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models

East China Normal University(华东师范大学);University of Warwick(华威大学)

Shujuan Zhao, Lingfeng Qiao, Kangyang Luo, Qian-Wen Zhang, Junru Lu, Di Yin

AI总结:

本文提出中文金融大模型 SNFinLLM,通过持续预训练、SFT 与 DPO 对齐提升问答、报告摘要、情感分析和金融计算能力,实验显示其优于现有金融大模型。

摘要:

大语言模型(LLM)已成为推动金融业自然语言处理应用的强大工具。然而,现有金融大语言模型往往面临幻觉或参数训练流于表面等挑战,导致性能欠佳,尤其是在金融计算和机器阅读理解(MRC)方面。为解决这些问题,我们提出了一种专为中文金融领域设计的新型大语言模型,名为 SNFinLLM。SNFinLLM 在回答问题、总结金融研究报告、分析情感以及执行金融计算等领域特定任务上表现出色。随后,我们开展监督微调(SFT),以提升模型在各类金融领域中的熟练程度。具体而言,我们收集了大量金融数据,并构建了一个由金融领域新闻文章、专业论文和研究报告组成的高质量指令数据集。我们利用领域特定数据集和通用数据集,在一个成熟的开源基础模型上继续进行预训练,得到 SNFinLLM-base。此后,我们进行监督微调(SFT),以增强模型在多项金融任务上的能力。关键在于,我们采用了一种简单直接的直接偏好优化(DPO)方法,使模型更好地与人类偏好对齐。在金融基准测试和我们的评估数据集上进行的大量实验表明,SNFinLLM 显著优于其他最先进的金融语言模型。更多详情请在此处观看我们的演示视频:https://www.youtube.com/watch?v=GYT-65HZwus。

英文摘要:

Large language models (LLMs) have become powerful tools for advancing natural language processing applications in the financial industry. However, existing financial LLMs often face challenges such as hallucinations or superficial parameter training, resulting in suboptimal performance, particularly in financial computing and machine reading comprehension (MRC). To address these issues, we propose a novel large language model specifically designed for the Chinese financial domain, named SNFinLLM. SNFinLLM excels in domain-specific tasks such as answering questions, summarizing financial research reports, analyzing sentiment, and executing financial calculations. We then perform the supervised fine-tuning (SFT) to enhance the model's proficiency across various financial domains. Specifically, we gather extensive financial data and create a high-quality instruction dataset composed of news articles, professional papers, and research reports of finance domain. Utilizing both domain-specific and general datasets, we proceed with continuous pre-training on an established open-source base model, resulting in SNFinLLM-base. Following this, we engage in supervised fine-tuning (SFT) to bolster the model's capability across multiple financial tasks. Crucially, we employ a straightforward Direct Preference Optimization (DPO) method to better align the model with human preferences. Extensive experiments conducted on finance benchmarks and our evaluation dataset demonstrate that SNFinLLM markedly outperforms other state-of-the-art financial language models. For more details, check out our demo video here: https://www.youtube.com/watch?v=GYT-65HZwus.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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