Artificial intelligence (AI) is widely deployed to solve problems related to
marketing attribution and budget optimization. However, AI models can be quite
complex, and it can be difficult to understand model workings and insights
without extensive implementation teams. In principle, recently developed large
language models (LLMs), like GPT-4, can be deployed to provide marketing
insights, reducing the time and effort required to make critical decisions. In
practice, there are substantial challenges that need to be overcome to reliably
use such models. We focus on domain-specific question-answering, SQL generation
needed for data retrieval, and tabular analysis and show how a combination of
semantic search, prompt engineering, and fine-tuning can be applied to
dramatically improve the ability of LLMs to execute these tasks accurately. We
compare both proprietary models, like GPT-4, and open-source models, like
Llama-2-70b, as well as various embedding methods. These models are tested on
sample use cases specific to marketing mix modeling and attribution.

人工智能广泛应用于解决与市场归因和预算优化相关的问题，然而由于模型复杂，很难在没有完整的实施团队的情况下理解模型的运作和见解，理论上，最近开发的大型语言模型（如 GPT-4）可以用来提供营销见解，以减少做出关键决策所需的时间和精力。 实际上，要可靠地使用这些模型，需要克服一些重大挑战，我们聚焦于领域特定的问答、用于数据检索的 SQL 生成以及表格分析，并展示了如何通过语义搜索、提示工程和微调的结合来显著提高大型语言模型执行这些任务的准确性。我们比较了专有模型（如 GPT-4）和开源模型（如 Llama-2-70b），以及各种嵌入方法。这些模型在特定于市场混合建模和归因的样本用例上进行了测试。

提升基于大型语言模型的市场分析副驾驶员的能力：语义搜索和微调

Improving the Capabilities of Large Language Model Based Marketing  Analytics Copilots With Semantic Search And Fine-Tuning

Auction-based Federated Learning (AFL) has emerged as an important research
field in recent years. The prevailing strategies for FL model users (MUs)
assume that the entire team of the required data owners (DOs) for an FL task
must be assembled before training can commence. In practice, an MU can trigger
the FL training process multiple times. DOs can thus be gradually recruited
over multiple FL model training sessions. Existing bidding strategies for AFL
MUs are not designed to handle such scenarios. Therefore, the problem of
multi-session AFL remains open. To address this problem, we propose the
Multi-session Budget Optimization Strategy for forward Auction-based Federated
Learning (MultiBOS-AFL). Based on hierarchical reinforcement learning,
MultiBOS-AFL jointly optimizes inter-session budget pacing and intra-session
bidding for AFL MUs, with the objective of maximizing the total utility.
Extensive experiments on six benchmark datasets show that it significantly
outperforms seven state-of-the-art approaches. On average, MultiBOS-AFL
achieves 12.28% higher utility, 14.52% more data acquired through auctions for
a given budget, and 1.23% higher test accuracy achieved by the resulting FL
model compared to the best baseline. To the best of our knowledge, it is the
first budget optimization decision support method with budget pacing capability
designed for MUs in multi-session forward auction-based federated learning

基于层次强化学习的多会话向前拍卖联邦学习（MultiBOS-AFL）通过优化会话间预算节奏和会话内竞标，旨在最大化总效用，实验证明其明显优于七种最先进方法，平均而言，MultiBOS-AFL 比最佳基准线的效用高出 12.28％、在给定预算内通过拍卖获得的数据多出 14.52％，以及最终 FL 模型的测试准确性比最佳基准线高出 1.23％。据我们所知，这是第一个设计用于多会话向前拍卖联邦学习中的带有预算节奏能力的预算优化决策支持方法。

基于前向拍卖的联邦学习多会话预算优化

Multi-Session Budget Optimization for Forward Auction-based Federated  Learning

We consider the budget optimization problem faced by an advertiser
participating in repeated sponsored search auctions, seeking to maximize the
number of clicks attained under that budget. We cast the budget optimization
problem as a Markov Decision Process (MDP) with censored observations, and
propose a learning algorithm based on the wellknown Kaplan-Meier or
product-limit estimator. We validate the performance of this algorithm by
comparing it to several others on a large set of search auction data from
Microsoft adCenter, demonstrating fast convergence to optimal performance.

本研究考虑广告主在参加重复竞价搜索拍卖时所面临的预算优化问题，将其作为带有被审查观测的马尔可夫决策过程（MDP）来建模，并提出基于 Kaplan-Meier 或产物极限估计器的学习算法。在微软广告中心的大量搜索竞拍数据上，将此算法与几种其他算法进行比较，证明其快速收敛的最佳性能。