LLMDistill4Ads: 使用交叉编码器从LLM信号中蒸馏以实现eBay广告商关键词推荐
LLMDistill4Ads: Using Cross-Encoders to Distill from LLM Signals for Advertiser Keyphrase Recommendations at eBay
AI总结：
LLMDistill4Ads通过交叉编码器从LLM信号中蒸馏，旨在提升eBay广告商关键词推荐的多样性和准确性。
摘要：
电子商务卖家被建议通过竞价关键词来提升其广告活动。这些关键词必须相关，以防止无关物品 clutter 搜索系统，以维持积极的卖家形象。关键短语建议必须与卖家、搜索和买家的判断相一致。鉴于这些系统中收集负面反馈的挑战，LLM已被用作可扩展的人类判断代理。我们对一家主要电子商务平台的蒸馏框架进行了实证研究，该框架包括一个LLM教师、一个交叉编码器助理和一个基于嵌入检索（EBR）的学生模型，旨在减轻点击引起的偏见，提供更多样化的关键词推荐，同时对广告、搜索和买家偏好进行对齐。
英文摘要：
E-commerce sellers are advised to bid on keyphrases to boost their advertising campaigns. These keyphrases must be relevant to prevent irrelevant items from cluttering Search systems and to maintain positive seller perception. It is vital that keyphrase suggestions align with seller, Search, and buyer judgments. Given the challenges in collecting negative feedback in these systems, LLMs have been used as a scalable proxy for human judgments. We present an empirical study on a major e-commerce platform of a distillation framework involving an LLM teacher, a cross-encoder assistant and a bi-encoder Embedding Based Retrieval (EBR) student model, aimed at mitigating click-induced biases and provide more diverse keyphrase recommendations while aligning advertising, search and buyer preferences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
