ZiGong 1.0: A Large Language Model for Financial Credit
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915658330013696 |
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| author | Lei, Yu Wang, Zixuan Liu, Chu Wang, Tongyao |
| author_facet | Lei, Yu Wang, Zixuan Liu, Chu Wang, Tongyao |
| contents | Large Language Models (LLMs) have demonstrated strong performance across various general Natural Language Processing (NLP) tasks. However, their effectiveness in financial credit assessment applications remains suboptimal, primarily due to the specialized financial expertise required for these tasks. To address this limitation, we propose ZiGong, a Mistral-based model enhanced through multi-task supervised fine-tuning. To specifically combat model hallucination in financial contexts, we introduce a novel data pruning methodology. Our approach utilizes a proxy model to score training samples, subsequently combining filtered data with original datasets for model training. This data refinement strategy effectively reduces hallucinations in LLMs while maintaining reliability in downstream financial applications. Experimental results show our method significantly enhances model robustness and prediction accuracy in real-world financial scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16159 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ZiGong 1.0: A Large Language Model for Financial Credit Lei, Yu Wang, Zixuan Liu, Chu Wang, Tongyao Computation and Language Computational Engineering, Finance, and Science Large Language Models (LLMs) have demonstrated strong performance across various general Natural Language Processing (NLP) tasks. However, their effectiveness in financial credit assessment applications remains suboptimal, primarily due to the specialized financial expertise required for these tasks. To address this limitation, we propose ZiGong, a Mistral-based model enhanced through multi-task supervised fine-tuning. To specifically combat model hallucination in financial contexts, we introduce a novel data pruning methodology. Our approach utilizes a proxy model to score training samples, subsequently combining filtered data with original datasets for model training. This data refinement strategy effectively reduces hallucinations in LLMs while maintaining reliability in downstream financial applications. Experimental results show our method significantly enhances model robustness and prediction accuracy in real-world financial scenarios. |
| title | ZiGong 1.0: A Large Language Model for Financial Credit |
| topic | Computation and Language Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2502.16159 |