AI 中文总结
研究广告自动出价问题，提出SAGE框架，通过位置增强、文本对齐和约束门控LoRA三个组件，实现参数高效多模态对齐，在大规模基准实验中性能卓越，调整参数少，消融研究验证各组件贡献。
AI 中文摘要
广告出价已从手动策略发展到更适用于大规模动态拍卖环境的自动出价系统。虽然大语言模型（LLMs）的最新进展为自动出价提供了有力推理，但现有方法存在轨迹-文本交互浅且需要高成本微调的问题，阻碍了在不同约束下对预训练知识的有效利用。为应对这些挑战，我们提出了SAGE，一种由LLMs指导的用于高效出价的新型策略感知自动出价框架。SAGE引入了用于受约束的LLMs自动出价的参数高效多模态对齐框架。具体包括三个关键组件：位置增强模块采用时间-语义位置嵌入来有效捕获内在动态和语义结构；文本对齐模块利用门控交叉注意力来对齐轨迹和文本模态的嵌入空间，实现有效的多模态融合并减轻长轨迹带来的计算开销；约束门控LoRA模块将约束用作路由信号，仅激活一小部分专家以有效调整冻结LLM的行为。在大规模自动出价基准上的大量实验表明，SAGE在调整不到完全微调所需可训练参数的10%的情况下始终实现卓越性能。消融研究进一步验证了每个组件对框架整体性能的关键贡献。
英文摘要
Advertising bidding has evolved from manual strategies to auto-bidding systems better adapted for large-scale, dynamic auction environments. While recent advances in Large Language Models (LLMs) offer strong reasoning for auto-bidding, existing methods suffer from shallow trajectory-text interactions and require costly fine-tuning, hindering the efficient use of pretrained knowledge under diverse constraints. To address these challenges, we propose SAGE, a novel Strategy-aware Auto-bidding framework Guided by LLMs for Efficient bidding. SAGE introduces a parameter-efficient multi-modal alignment framework for constrained auto-bidding with LLMs. Specifically, SAGE comprises three key components: (i) the position augmentation module adopts temporal-semantic positional embeddings to effectively capture the intrinsic dynamics and semantic structures; (ii) the text alignment module leverages gated cross-attention to align the embedding spaces of trajectory and text modalities, enabling effective multi-modal fusion while alleviating the computational overhead caused by long trajectories; (iii) the constraint-gated LoRA module employs constraints as routing signals, activating only a small subset of experts to adapt the behavior of a frozen LLM efficiently. Extensive experiments on large-scale auto-bidding benchmark demonstrate that SAGE consistently achieves superior performance while tuning less than 10% of the trainable parameters required by full fine-tuning. Ablation studies further validate the critical contribution of each component to the framework's overall performance.
