发表机构
Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究电商产品目录结构化属性缺失值预测问题，提出CatalogAgent系统，通过监督者调解冲突、记忆库和汇总器实现自学习，经上下文工程将监督者能力转移到生成器和评估器，有效提升其性能。
AI 中文摘要
产品目录是电子商务网站的支柱，但大量结构化属性（如材质、颜色和形状）往往存在缺失值。基于大语言模型（LLM）的生成器-评估器框架在预测结构化属性值时存在挑战，当两者输出冲突时，难以处理。为此提出CatalogAgent系统，通过监督代理调解冲突并做出最终决策，同时结合记忆库和记忆汇总器实现自学习。通过上下文工程，将监督者能力转移到生成器和评估器，分别提高了15.24%和13.98%的性能。实验证明了监督代理介导的自学习系统可提高生成式人工智能模型准确性的新范式。
英文摘要
Product catalogs are the backbone of e-commerce sites, yet a large number of structured attributes (SAs) -- such as material, color, and shape -- often have missing values. Typically, SA values are extracted from product information, including titles and descriptions. While LLM-based generator-evaluator frameworks have demonstrated effectiveness for SA prediction -- where an LLM generates SA values and another evaluates them -- they face challenges when the Generator and Evaluator produce conflicting outputs, as either component can make mistakes. We introduce \texttt{CatalogAgent}, a novel agentic system that continuously improves Generator and Evaluator models for e-commerce catalog enrichment. When disagreements arise from (1) internal conflicts between the LLM-based Generator and Evaluator, or (2) external feedback from sellers on LLM outputs, a Supervisor Agent intervenes to mediate these conflicts and make final decisions. The system also incorporates a Memory Base and a Memory Summarizer that stores Supervisor Agent activities from individual cases and aggregates patterns into learnings. These learnings are fed back to the worker Generator and Evaluator LLMs, enabling self-improvement without human intervention. Through context engineering -- injecting learnings and insights into worker LLMs' contexts -- the system successfully transfers the Supervisor's capabilities to the Generator and Evaluator, improving their performance by 15.24\% and 13.98\%, respectively. Our experiments demonstrate a new paradigm of Supervisor Agent-mediated self-learning systems for improving generative AI model accuracy.
