Efficient LLM-based Advertising via Model Compression and Parallel Verification
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910211197894656 |
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| author | Dong, Wenxin Gao, Chang Yu, Guanghui Jiao, Xuewu Hu, Mingqing Fu, Qiang Xu, Peng Wei, Penghui Xu, Hui Xing, Yue Li, Shuanglong Liu, Lin |
| author_facet | Dong, Wenxin Gao, Chang Yu, Guanghui Jiao, Xuewu Hu, Mingqing Fu, Qiang Xu, Peng Wei, Penghui Xu, Hui Xing, Yue Li, Shuanglong Liu, Lin |
| contents | Large language models (LLMs) have shown remarkable potential in advertising scenarios such as ad creative generation and targeted advertising. However, deploying LLMs in real-time advertising systems poses significant challenges due to their high inference latency and computational cost. In this paper, we propose an Efficient Generative Targeting framework that integrates adaptive group quantization, layer-adaptive hierarchical sparsification, and prefix-tree parallel verification to accelerate LLM inference while preserving generation quality. Extensive experiments on two real-world advertising scenarios demonstrate that our framework achieves significant speedup with acceptable quality degradation, making it operationally viable for practical deployments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11582 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Efficient LLM-based Advertising via Model Compression and Parallel Verification Dong, Wenxin Gao, Chang Yu, Guanghui Jiao, Xuewu Hu, Mingqing Fu, Qiang Xu, Peng Wei, Penghui Xu, Hui Xing, Yue Li, Shuanglong Liu, Lin Computation and Language I.2.7; H.3.5 Large language models (LLMs) have shown remarkable potential in advertising scenarios such as ad creative generation and targeted advertising. However, deploying LLMs in real-time advertising systems poses significant challenges due to their high inference latency and computational cost. In this paper, we propose an Efficient Generative Targeting framework that integrates adaptive group quantization, layer-adaptive hierarchical sparsification, and prefix-tree parallel verification to accelerate LLM inference while preserving generation quality. Extensive experiments on two real-world advertising scenarios demonstrate that our framework achieves significant speedup with acceptable quality degradation, making it operationally viable for practical deployments. |
| title | Efficient LLM-based Advertising via Model Compression and Parallel Verification |
| topic | Computation and Language I.2.7; H.3.5 |
| url | https://arxiv.org/abs/2605.11582 |