Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce

• URL: http://arxiv.org/abs/2503.13518v1
• Date: Fri, 14 Mar 2025 18:22:43 GMT
• Title: Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-Commerce
• Authors: Jingying Zeng, Zhenwei Dai, Hui Liu, Samarth Varshney, Zhiji Liu, Chen Luo, Zhen Li, Qi He, Xianfeng Tang,
• Abstract summary: Examples as the Prompt (EaP) is a framework that leverages labeled data to enhance prompts.<n>EaP achieves comparable or even superior performance compared to hand-crafted prompts.<n>EaP_lite replaces the natural language components of prompts with labeled examples.
• Score: 14.436208311342261
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prompting LLMs offers an efficient way to guide output generation without explicit model training. In the e-commerce domain, prompting-based applications are widely used for tasks such as query understanding, recommender systems, and customer support. However, adapting LLMs to different tasks often requires extensive prompt engineering by domain experts, along with frequent updates to align with evolving business needs. Additionally, crafting fully unbiased natural language prompts remains a challenge for humans. To address these challenges, we propose a novel framework, Examples as the Prompt (EaP) which leverages labeled data to enhance prompts. Specifically, EaP automatically selects the most representative examples to maximize the few-shot capability of LLMs. It is efficient due to its unsupervised example selection and adaptive to potential data distribution shifts. We validate EaP on four real-world production use cases, demonstrating that it achieves comparable or even superior performance comparing to hand-crafted prompts designed by domain experts. Additionally, we introduce EaP_lite, which entirely replaces the natural language components of prompts with labeled examples. EaP_lite improves LLM inference speed by up to 70% without compromising performance. Latest online A/B test shows that using EaP and EaP_lite for data labeling can bring significant composite revenue gain by 0.06%.

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