LAMA Framework Auctions Ads Token-by-Token During LLM Generation
Researchers from Renmin University and Stanford propose LAMA, a large-model-native advertising framework that auctions ads per generated token, integrating ad allocation into the generation process. This shifts from traditional pre-defined ad slots to dynamic allocation where each token influences both content continuation and ad exposure.
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机器之心机器之心
搜索结果里但凡出现 AI 摘要，用户点开普通网页的几率就几乎腰斩。 Pew Research Center 对美国用户在 Google 上大规模浏览行为的分析发现：有 AI 摘要时，用户点击传统搜索结果的比例从 15%直接暴跌至 8%。[1] 用户正在对话里完成探索和比较，广告也跟着迁移了过来。OpenAI 已经正式上线 ChatGPT Ads，Google 也将广告引入了 AI Overviews，他们给回答挂上赞助商的小卡片，直接延续了搜索引擎时代的模式。[2, 3] 但人们获取信息的范式转变，让人不禁开始重新思考广告的形态： 当信息媒介从整页搜索结果变成一段实时生成的回答，广告究竟应该拍卖什么？ 三十年来，在线广告始终建立在一个默认前提之上： 广告位先存在，拍卖再决定谁获得它 。 搜索结果的名次、信息流中的哪一次展示，都是预先定义好的机会。但大模型的回答是逐字生成的：某个品牌是否适合出现、在哪里出现、用什么方式介绍，都取决于前面已经写了什么。甚至先谈价格或是先谈体验，都可能为产品造成完全不同的印象。 图 1：在生成式信息媒介里，广告机会在哪出现、由谁获得、如何呈现，会共同影响营销效果与用户体验。 因此，生成不只是填充广告机会，还在塑造广告机会。 在这个新范式下，已有研究开始探索段落级广告插入、候选回答竞价等拍卖方式，而来自人大高瓴和斯坦福大学的研究团队，将大模型原生广告的竞争进一步推进到了逐 Token 的生成过程中。 他们提出的 LAMA ，让内容生成与广告分配一同演变：每当生成一个 Token，都在实时影响回答怎样续写，也在影响最终谁获得广告曝光。这就是这项工作提出的核心思路—— 生成即分配（Generation as Allocation） 。 标题：Token-Level Advertising 作者：Hanbing Liu、Bowei Zhang、Changyuan Yu、Yinyu Ye、Qi Qi 单位：中国人民大学高瓴人工智能学院、斯坦福大学 链接：https://arxiv.org/abs/2608.27382 大模型每写一个字都是一次竞价过程 把广告拍卖推进到 Token 级，广告主争夺的就不只是最后的曝光机会，还有 回答接下来往哪个方向写 。比如，同样是推荐度假目的地，先谈交通还是先谈住宿，可能让不同广告主在后续竞争中占据优势。广告主也因
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