Strategy-Aware Parameter-Efficient Adaptation for LLM-based Auto-Bidding
2026-07-27 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors propose SAGE, a new system for automated advertising bidding that uses large language models (LLMs) more efficiently. Their method improves how bidding strategies understand and combine both time-based data and text information without needing expensive retraining. SAGE uses special modules to better capture bidding patterns, align different types of data, and handle constraints smartly while only adjusting a small part of the model. Tests show that SAGE works better than previous methods while being less resource-intensive.
auto-biddinglarge language modelsmulti-modal alignmenttemporal-semantic embeddingscross-attentionLoRAparameter-efficient tuningtrajectory dataconstraint-based routing
Authors
Songyue Cai, Lianyu Wang, Shan Gu, Ziru Xu, Jian Xu, Xiaofeng Zhu, Bo Zheng
Abstract
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.