CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914254320304128 |
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| author | Lin, Ryan Feng Tian, Keyu Zheng, Hanming Zhang, Congjing Zeng, Li Huang, Shuai |
| author_facet | Lin, Ryan Feng Tian, Keyu Zheng, Hanming Zhang, Congjing Zeng, Li Huang, Shuai |
| contents | The emergence of large language models (LLMs) has sparked much interest in creating LLM-based digital populations that can be applied to many applications such as social simulation, crowdsourcing, marketing, and recommendation systems. A digital population can reduce the cost of recruiting human participants and alleviate many concerns related to human subject study. However, research has found that most of the existing works rely solely on LLMs and could not sufficiently capture the accuracy and diversity of a real human population. To address this limitation, we propose CrowdLLM that integrates pretrained LLMs and generative models to enhance the diversity and fidelity of the digital population. We conduct theoretical analysis of CrowdLLM regarding its great potential in creating cost-effective, sufficiently representative, scalable digital populations that can match the quality of a real crowd. Comprehensive experiments are also conducted across multiple domains (e.g., crowdsourcing, voting, user rating) and simulation studies which demonstrate that CrowdLLM achieves promising performance in both accuracy and distributional fidelity to human data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models Lin, Ryan Feng Tian, Keyu Zheng, Hanming Zhang, Congjing Zeng, Li Huang, Shuai Multiagent Systems Artificial Intelligence Machine Learning Methodology The emergence of large language models (LLMs) has sparked much interest in creating LLM-based digital populations that can be applied to many applications such as social simulation, crowdsourcing, marketing, and recommendation systems. A digital population can reduce the cost of recruiting human participants and alleviate many concerns related to human subject study. However, research has found that most of the existing works rely solely on LLMs and could not sufficiently capture the accuracy and diversity of a real human population. To address this limitation, we propose CrowdLLM that integrates pretrained LLMs and generative models to enhance the diversity and fidelity of the digital population. We conduct theoretical analysis of CrowdLLM regarding its great potential in creating cost-effective, sufficiently representative, scalable digital populations that can match the quality of a real crowd. Comprehensive experiments are also conducted across multiple domains (e.g., crowdsourcing, voting, user rating) and simulation studies which demonstrate that CrowdLLM achieves promising performance in both accuracy and distributional fidelity to human data. |
| title | CrowdLLM: Building LLM-Based Digital Populations Augmented with Generative Models |
| topic | Multiagent Systems Artificial Intelligence Machine Learning Methodology |
| url | https://arxiv.org/abs/2512.07890 |