In the field of computational advertising, the integration of ads into the
outputs of large language models (LLMs) presents an opportunity to support
these services without compromising content integrity. This paper introduces
novel auction mechanisms for ad allocation and pricing within the textual
outputs of LLMs, leveraging retrieval-augmented generation (RAG). We propose a
segment auction where an ad is probabilistically retrieved for each discourse
segment (paragraph, section, or entire output) according to its bid and
relevance, following the RAG framework, and priced according to competing bids.
We show that our auction maximizes logarithmic social welfare, a new notion of
welfare that balances allocation efficiency and fairness, and we characterize
the associated incentive-compatible pricing rule. These results are extended to
multi-ad allocation per segment. An empirical evaluation validates the
feasibility and effectiveness of our approach over several ad auction
scenarios, and exhibits inherent tradeoffs in metrics as we allow the LLM more
flexibility to allocate ads.

该论文介绍了在大型语言模型中将广告集成到输出中的机制，提出了一种基于拍卖的广告分配和定价方法，通过检索增强生成（RAG）在段落、章节或整个输出中根据出价和相关性概率性地检索广告并根据竞争出价定价，展示了最大化对数社会福利的拍卖机制，并表征了相关的激励兼容定价规则。实证评估验证了我们的方法在多种广告拍卖场景中的可行性和有效性，并展示了在允许大型语言模型更灵活地分配广告时的度量指标之间固有的权衡。

通过检索增强生成技术的广告拍卖

Ad Auctions for LLMs via Retrieval Augmented Generation

E-commerce platforms usually present an ordered list, mixed with several
organic items and an advertisement, in response to each user's page view
request. This list, the outcome of ad auction and allocation processes,
directly impacts the platform's ad revenue and gross merchandise volume (GMV).
Specifically, the ad auction determines which ad is displayed and the
corresponding payment, while the ad allocation decides the display positions of
the advertisement and organic items. The prevalent methods of segregating the
ad auction and allocation into two distinct stages face two problems: 1) Ad
auction does not consider externalities, such as the influence of actual
display position and context on ad Click-Through Rate (CTR); 2) The ad
allocation, which utilizes the auction-winning ad's payment to determine the
display position dynamically, fails to maintain incentive compatibility (IC)
for the advertisement. For instance, in the auction stage employing the
traditional Generalized Second Price (GSP) , even if the winning ad increases
its bid, its payment remains unchanged. This implies that the advertisement
cannot secure a better position and thus loses the opportunity to achieve
higher utility in the subsequent ad allocation stage. Previous research often
focused on one of the two stages, neglecting the two-stage problem, which may
result in suboptimal outcomes...

电子商务平台上的广告拍卖和分配过程对广告收入和总商品交易量有直接影响，存在两个问题：广告拍卖阶段不考虑外部因素（例如实际显示位置和上下文对广告点击率的影响），广告分配阶段无法实现广告的激励兼容性。以往的研究多关注其中一个阶段，忽视了这个两个阶段的问题，从而导致了次优的结果。

深度自动化机制设计 --- 用于广告竞价和分配的新闻推送

Deep Automated Mechanism Design for Integrating Ad Auction and  Allocation in Feed

Constrained $k$-submodular maximization is a general framework that captures
many discrete optimization problems such as ad allocation, influence
maximization, personalized recommendation, and many others. In many of these
applications, datasets are large or decisions need to be made in an online
manner, which motivates the development of efficient streaming and online
algorithms. In this work, we develop single-pass streaming and online
algorithms for constrained $k$-submodular maximization with both monotone and
general (possibly non-monotone) objectives subject to cardinality and knapsack
constraints. Our algorithms achieve provable constant-factor approximation
guarantees which improve upon the state of the art in almost all settings.
Moreover, they are combinatorial and very efficient, and have optimal space and
running time. We experimentally evaluate our algorithms on instances for ad
allocation and other applications, where we observe that our algorithms are
efficient and scalable, and construct solutions that are comparable in value to
offline greedy algorithms.

本文提出了单遍流式和在线算法的受约束 k - 次模最大化，其中包含基数和背包约束限制，该算法可以提供不错的近似解和高效的解决方案，并在广告分配等应用实例上得到了验证。