@inproceedings{wang-etal-2025-icl,
title = "{ICL}-Bandit: Relevance Labeling in Advertisement Recommendation Systems via {LLM}",
author = "Wang, Lu and
Duan, Chiming and
Zhao, Pu and
Yang, Fangkai and
Shi, Yong and
Luo, Xuefeng and
Xu, Bingjing and
Deng, Weiwei and
Lin, Qingwei and
Zhang, Dongmei",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1273/",
pages = "23439--23449",
ISBN = "979-8-89176-335-7",
abstract = "Measuring the relevance between user queries and advertisements is a critical task for advertisement (ad) recommendation systems, such as Microsoft Bing Ads and Google Ads. Traditionally, this requires expert data labeling, which is both costly and time-consuming. Recent advances have explored using Large Language Models (LLMs) for labeling, but these models often lack domain-specific knowledge. In-context learning (ICL), which involves providing a few demonstrations, is a common practice to enhance LLM performance on domain-specific tasks. However, retrieving high-quality demonstrations in a vast exploration space remains challenging. In this paper, we introduce ICL-Bandit, a practical and effective approach that leverages ICL to enhance the query-ad relevance labeling capabilities of LLMs. We develop a novel bandit learning method to identify and provide superior demonstrations for ICL, thereby improving labeling performance. Experimental results demonstrate that ICL-Bandit achieves state-of-the-art performance compared to existing methods. Additionally, ICL-Bandit has been deployed in Company X, that serves billions of users worldwide, confirming its robustness and effectiveness."
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<abstract>Measuring the relevance between user queries and advertisements is a critical task for advertisement (ad) recommendation systems, such as Microsoft Bing Ads and Google Ads. Traditionally, this requires expert data labeling, which is both costly and time-consuming. Recent advances have explored using Large Language Models (LLMs) for labeling, but these models often lack domain-specific knowledge. In-context learning (ICL), which involves providing a few demonstrations, is a common practice to enhance LLM performance on domain-specific tasks. However, retrieving high-quality demonstrations in a vast exploration space remains challenging. In this paper, we introduce ICL-Bandit, a practical and effective approach that leverages ICL to enhance the query-ad relevance labeling capabilities of LLMs. We develop a novel bandit learning method to identify and provide superior demonstrations for ICL, thereby improving labeling performance. Experimental results demonstrate that ICL-Bandit achieves state-of-the-art performance compared to existing methods. Additionally, ICL-Bandit has been deployed in Company X, that serves billions of users worldwide, confirming its robustness and effectiveness.</abstract>
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%0 Conference Proceedings
%T ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM
%A Wang, Lu
%A Duan, Chiming
%A Zhao, Pu
%A Yang, Fangkai
%A Shi, Yong
%A Luo, Xuefeng
%A Xu, Bingjing
%A Deng, Weiwei
%A Lin, Qingwei
%A Zhang, Dongmei
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F wang-etal-2025-icl
%X Measuring the relevance between user queries and advertisements is a critical task for advertisement (ad) recommendation systems, such as Microsoft Bing Ads and Google Ads. Traditionally, this requires expert data labeling, which is both costly and time-consuming. Recent advances have explored using Large Language Models (LLMs) for labeling, but these models often lack domain-specific knowledge. In-context learning (ICL), which involves providing a few demonstrations, is a common practice to enhance LLM performance on domain-specific tasks. However, retrieving high-quality demonstrations in a vast exploration space remains challenging. In this paper, we introduce ICL-Bandit, a practical and effective approach that leverages ICL to enhance the query-ad relevance labeling capabilities of LLMs. We develop a novel bandit learning method to identify and provide superior demonstrations for ICL, thereby improving labeling performance. Experimental results demonstrate that ICL-Bandit achieves state-of-the-art performance compared to existing methods. Additionally, ICL-Bandit has been deployed in Company X, that serves billions of users worldwide, confirming its robustness and effectiveness.
%U https://aclanthology.org/2025.findings-emnlp.1273/
%P 23439-23449
Markdown (Informal)
[ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM](https://aclanthology.org/2025.findings-emnlp.1273/) (Wang et al., Findings 2025)
ACL
- Lu Wang, Chiming Duan, Pu Zhao, Fangkai Yang, Yong Shi, Xuefeng Luo, Bingjing Xu, Weiwei Deng, Qingwei Lin, and Dongmei Zhang. 2025. ICL-Bandit: Relevance Labeling in Advertisement Recommendation Systems via LLM. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 23439–23449, Suzhou, China. Association for Computational Linguistics.