Research on knowledge-augmented Chinese financial large language model
The financial industry has long faced challenges in processing vast amounts of market data and information. Currently, large language models have made significant progress in general text understanding tasks, but there is still considerable room for improvement in more specialized domains, such as C...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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China InfoCom Media Group
2025-03-01
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| Series: | 大数据 |
| Subjects: | |
| Online Access: | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2025021 |
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| _version_ | 1849731667477921792 |
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| author | CHENG Dawei JIA Renjun LI Jiangtong DING Zhijun JIANG Changjun |
| author_facet | CHENG Dawei JIA Renjun LI Jiangtong DING Zhijun JIANG Changjun |
| author_sort | CHENG Dawei |
| collection | DOAJ |
| description | The financial industry has long faced challenges in processing vast amounts of market data and information. Currently, large language models have made significant progress in general text understanding tasks, but there is still considerable room for improvement in more specialized domains, such as Chinese finance. To address the limitations of current large language models in handling professional domain-specific text tasks, a two-stage training approach based on finance knowledge-enhanced continued pre-training and supervised fine-tuning is designed. This approach improves the organization of training data and the training paradigm, thereby enhancing the model's capabilities in complex financial scenarios. Finally, experiments have validated the effectiveness of the proposed knowledge-enhanced approach in large model training. |
| format | Article |
| id | doaj-art-b61fa4b6170546f4adef07286c49c977 |
| institution | DOAJ |
| issn | 2096-0271 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | China InfoCom Media Group |
| record_format | Article |
| series | 大数据 |
| spelling | doaj-art-b61fa4b6170546f4adef07286c49c9772025-08-20T03:08:28ZzhoChina InfoCom Media Group大数据2096-02712025-03-011151886967683Research on knowledge-augmented Chinese financial large language modelCHENG DaweiJIA RenjunLI JiangtongDING ZhijunJIANG ChangjunThe financial industry has long faced challenges in processing vast amounts of market data and information. Currently, large language models have made significant progress in general text understanding tasks, but there is still considerable room for improvement in more specialized domains, such as Chinese finance. To address the limitations of current large language models in handling professional domain-specific text tasks, a two-stage training approach based on finance knowledge-enhanced continued pre-training and supervised fine-tuning is designed. This approach improves the organization of training data and the training paradigm, thereby enhancing the model's capabilities in complex financial scenarios. Finally, experiments have validated the effectiveness of the proposed knowledge-enhanced approach in large model training.http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2025021large language modelfinancial time series forecasting |
| spellingShingle | CHENG Dawei JIA Renjun LI Jiangtong DING Zhijun JIANG Changjun Research on knowledge-augmented Chinese financial large language model 大数据 large language model financial time series forecasting |
| title | Research on knowledge-augmented Chinese financial large language model |
| title_full | Research on knowledge-augmented Chinese financial large language model |
| title_fullStr | Research on knowledge-augmented Chinese financial large language model |
| title_full_unstemmed | Research on knowledge-augmented Chinese financial large language model |
| title_short | Research on knowledge-augmented Chinese financial large language model |
| title_sort | research on knowledge augmented chinese financial large language model |
| topic | large language model financial time series forecasting |
| url | http://www.j-bigdataresearch.com.cn/thesisDetails#10.11959/j.issn.2096-0271.2025021 |
| work_keys_str_mv | AT chengdawei researchonknowledgeaugmentedchinesefinanciallargelanguagemodel AT jiarenjun researchonknowledgeaugmentedchinesefinanciallargelanguagemodel AT lijiangtong researchonknowledgeaugmentedchinesefinanciallargelanguagemodel AT dingzhijun researchonknowledgeaugmentedchinesefinanciallargelanguagemodel AT jiangchangjun researchonknowledgeaugmentedchinesefinanciallargelanguagemodel |