Large Language Model Sourcing: A Survey
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911346240520192 |
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| author | Pang, Liang Gu, Jia Dai, Sunhao Wei, Zihao Duan, Zenghao Wu, Kangxi Yin, Zhiyi Xu, Jun Shen, Huawei Cheng, Xueqi |
| author_facet | Pang, Liang Gu, Jia Dai, Sunhao Wei, Zihao Duan, Zenghao Wu, Kangxi Yin, Zhiyi Xu, Jun Shen, Huawei Cheng, Xueqi |
| contents | Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspectives is essential. This survey presents a systematic investigation organized around four interrelated dimensions: Model Sourcing, Model Structure Sourcing, Training Data Sourcing, and External Data Sourcing. Moreover, a unified dual-paradigm taxonomy is proposed that classifies existing sourcing methods into prior-based (proactive traceability embedding) and posterior-based (retrospective inference) approaches. Traceability across these dimensions enhances the transparency, accountability, and trustworthiness of LLMs deployment in real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model Sourcing: A Survey Pang, Liang Gu, Jia Dai, Sunhao Wei, Zihao Duan, Zenghao Wu, Kangxi Yin, Zhiyi Xu, Jun Shen, Huawei Cheng, Xueqi Computation and Language Artificial Intelligence Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspectives is essential. This survey presents a systematic investigation organized around four interrelated dimensions: Model Sourcing, Model Structure Sourcing, Training Data Sourcing, and External Data Sourcing. Moreover, a unified dual-paradigm taxonomy is proposed that classifies existing sourcing methods into prior-based (proactive traceability embedding) and posterior-based (retrospective inference) approaches. Traceability across these dimensions enhances the transparency, accountability, and trustworthiness of LLMs deployment in real-world applications. |
| title | Large Language Model Sourcing: A Survey |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.10161 |