用大模型实时生成广告关键词，自动优化多指标表现
OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
- 基于大模型代理实时生成关键词，无需预训练数据
- 同时优化点击、转化等多指标，提升广告效果
- 自反思机制保障关键词质量，适合广告自动化场景
赞助式搜索广告中的关键词决策对广告活动成败至关重要。尽管基于大模型的方法可实现关键词自动生成，但仍存在三大瓶颈：依赖大规模查询-关键词对数据、缺乏在线多目标性能监控与优化能力、关键词选择质量控制较弱。这些限制阻碍了大模型在广告决策中通过监控和推理关键绩效指标（如曝光量、点击量、转化率、行动号召有效性）实现完全自动化。为此，我们提出OMS框架，具备三个特性：即时性（无需训练数据，实时监控并动态调整）、多目标性（通过代理式推理优化多个绩效指标）和自反思性（代理自主评估关键词质量）。在基准测试和真实广告活动上的实验表明，OMS优于现有方法；消融实验与人工评估验证了各组件有效性及生成关键词的质量。
原文摘要 · Abstract (English)
Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. While LLM-based methods offer automated keyword generation, they face three major limitations: reliance on large-scale query-keyword pair data, lack of online multi-objective performance monitoring and optimization, and weak quality control in keyword selection. These issues hinder the agentic use of LLMs in fully automating keyword decisions by monitoring and reasoning over key performance indicators such as impressions, clicks, conversions, and CTA effectiveness. To overcome these challenges, we propose OMS, a keyword generation framework that is On-the-fly (requires no training data, monitors online performance, and adapts accordingly), Multi-objective (employs agentic reasoning to optimize keywords based on multiple performance metrics), and Self-reflective (agentically evaluates keyword quality). Experiments on benchmarks and real-world ad campaigns show that OMS outperforms existing methods; ablation and human evaluations confirm the effectiveness of each component and the quality of generated keywords.
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