The advent of large language models (LLMs) brings an opportunity to minimize the effort in search engine result page (SERP) organization. In this paper, we propose GenSERP, a framework that leverages LLMs with vision in a few-shot setting to dynamically organize intermediate search results, including generated chat answers, website snippets, multimedia data, knowledge panels into a coherent SERP layout based on a user's query. Our approach has three main stages: (1) An information gathering phase where the LLM continuously orchestrates API tools to retrieve different types of items, and proposes candidate layouts based on the retrieved items, until it's confident enough to generate the final result. (2) An answer generation phase where the LLM populates the layouts with the retrieved content. In this phase, the LLM adaptively optimize the ranking of items and UX configurations of the SERP. Consequently, it assigns a location on the page to each item, along with the UX display details. (3) A scoring phase where an LLM with vision scores all the generated SERPs based on how likely it can satisfy the user. It then send the one with highest score to rendering. GenSERP features two generation paradigms. First, coarse-to-fine, which allow it to approach optimal layout in a more manageable way, (2) beam search, which give it a better chance to hit the optimal solution compared to greedy decoding. Offline experimental results on real-world data demonstrate how LLMs can contextually organize heterogeneous search results on-the-fly and provide a promising user experience.

翻译：大语言模型（LLMs）的诞生为简化搜索引擎结果页面（SERP）组织提供了契机。本文提出GenSERP框架，该框架在少样本场景下利用具备视觉能力的大语言模型，根据用户查询动态组织中间搜索结果（包括生成的聊天回答、网站摘要、多媒体数据、知识面板），将其整合为连贯的SERP布局。本方法包含三个主要阶段：（1）信息收集阶段——LLM持续编排API工具检索不同类型的条目，并基于检索结果提出候选布局，直至具备足够信心生成最终结果；（2）答案生成阶段——LLM用检索内容填充布局。在此阶段，LLM自适应优化条目排序与SERP的用户体验配置，为每个条目分配页面位置及UX显示细节；（3）评分阶段——具备视觉能力的LLM基于满足用户需求的可能性对所有生成的SERP进行评分，并将最高分结果发送至渲染环节。GenSERP包含两种生成范式：首先，由粗到细范式使其能够以更可控的方式逼近最优布局；其次，束搜索范式相比贪心解码更利于找到最优解。基于真实数据的离线实验结果表明，LLM能够根据上下文动态组织异构搜索结果，并提供令人期待的用户体验。